Method and system for using artificial intelligence and machine learning to provide recommendations to a healthcare provider in or near real-time during a telemedicine session

ABSTRACT

A computer-implemented system includes a treatment device configured to be manipulated by a user while the user performs a treatment plan, a patient interface, and a computing device configured to: receive treatment; write to an associated memory, configured to be accessed by an artificial intelligence engine, treatment data, the artificial intelligence engine being configured to use at least one machine learning model to, using the treatment data, generate at least one of a treatment scheduling output prediction and an appointment output; receive, from the artificial intelligence engine, the at least one of the treatment scheduling output prediction and the appointment output; and selectively modify, using the at least one of the treatment scheduling output prediction and the appointment output, the at least one aspect of the treatment plan.

CROSS-REFERENCES TO RELATED APPLICATIONS

This Continuation-In-Part Patent Application claims priority to and thebenefit of U.S. patent application Ser. No. 17/021,895 filed Sep. 15,2020, titled “Telemedicine for Orthopedic Treatment”, which claimspriority to and the benefit of U.S. Provisional Application Patent Ser.No. 62/910,232 filed Oct. 3, 2019, titled “Telemedicine for OrthopedicTreatment”, the entire disclosures of which are hereby incorporated byreference.

BACKGROUND

Remote medical assistance, also referred to, inter alia, as remotemedicine, telemedicine, telemed, telmed, tel-med, or telehealth, is anat least two-way communication between a healthcare provider orproviders, such as a physician or a physical therapist, and a patientusing audio and/or audiovisual and/or other sensorial or perceptive(e.g., tactile, gustatory, haptic, pressure-sensing-based orelectromagnetic (e.g., neurostimulation) communications (e.g., via acomputer, a smartphone, or a tablet).

SUMMARY

An aspect of the disclosed embodiments includes a computer-implementedsystem. The computer-implemented system may include a treatment deviceconfigured to be manipulated by a user while the user performs atreatment plan, and a patient interface. The patient interface maycomprise an output device configured to present telemedicine informationassociated with a telemedicine session. The computer-implemented systemmay also include a computing device configured to: receive treatmentdata pertaining to a user who uses the treatment device to perform thetreatment plan, wherein the treatment data comprises at least one ofcharacteristics of the user, measurement information pertaining to theuser, characteristics of the treatment device, and at least one aspectof the treatment plan, wherein the at least one aspect of the treatmentplan includes at least one of a treatment schedule and at least oneappointment; write to an associated memory, configured to be accessed byan artificial intelligence engine, the treatment data, the artificialintelligence engine being configured to use at least one machinelearning model to, using the treatment data, generate at least one of atreatment scheduling output prediction and an appointment output;receive, from the artificial intelligence engine, the at least one ofthe treatment scheduling output prediction and the appointment output;and selectively modify, using the at least one of the treatmentscheduling output prediction and the appointment output, the at leastone aspect of the treatment plan.

Another aspect of the disclosed embodiments includes a method thatcomprises receiving treatment data pertaining to a user who uses atreatment device to perform a treatment plan. The treatment data mayinclude at least one of characteristics of the user, measurementinformation pertaining to the user, characteristics of the treatmentdevice, and at least one aspect of the treatment plan. The at least oneaspect of the treatment plan may include at least one of a treatmentschedule and at least one appointment. The method may also includewriting to an associated memory, configured to be accessed by anartificial intelligence engine, the treatment data. The artificialintelligence engine may be configured to use at least one machinelearning model to generate, using the treatment data, at least one of atreatment scheduling output prediction and an appointment output. Themethod may also include receiving, from the artificial intelligenceengine, the at least one of the treatment scheduling output predictionand the appointment output. The method may also include selectivelymodifying, using the at least one of the treatment scheduling outputprediction and the appointment output, the at least one aspect of thetreatment plan.

Another aspect of the disclosed embodiments includes a system thatincludes a processing device and a memory communicatively coupled to theprocessing device and capable of storing instructions. The processingdevice executes the instructions to perform any of the methods,operations, or steps described herein.

Another aspect of the disclosed embodiments includes a tangible,non-transitory computer-readable medium storing instructions that, whenexecuted, cause a processing device to perform any of the methods,operations, or steps described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is best understood from the following detaileddescription when read in conjunction with the accompanying drawings. Itis emphasized that, according to common practice, the various featuresof the drawings are not to-scale. On the contrary, the dimensions of thevarious features are arbitrarily expanded or reduced for clarity.

FIG. 1 generally illustrates a block diagram of an embodiment of acomputer-implemented system for managing a treatment plan according tothe principles of the present disclosure.

FIG. 2 generally illustrates a perspective view of an embodiment of atreatment device according to the principles of the present disclosure.

FIG. 3 generally illustrates a perspective view of a pedal of thetreatment device of FIG. 2 according to the principles of the presentdisclosure.

FIG. 4 generally illustrates a perspective view of a person using thetreatment device of FIG. 2 according to the principles of the presentdisclosure.

FIG. 5 generally illustrates an example embodiment of an overviewdisplay of an assistant interface according to the principles of thepresent disclosure.

FIG. 6 generally illustrates an example block diagram of training amachine learning model to output, based on data pertaining to thepatient, a treatment plan for the patient according to the principles ofthe present disclosure.

FIG. 7 generally illustrates an embodiment of an overview display of theassistant interface presenting recommended treatment plans and excludedtreatment plans in real-time during a telemedicine session according tothe principles of the present disclosure.

FIG. 8 generally illustrates an embodiment of the overview display ofthe assistant interface presenting, in real-time during a telemedicinesession, recommended treatment plans that have changed as a result ofpatient data changing according to the principles of the presentdisclosure.

FIG. 9 is a flow diagram generally illustrating a method for providing,based on treatment data pertaining to a user who uses the treatmentdevice of FIG. 2, a recommendation to a healthcare provider according tothe principles of the present disclosure.

FIG. 10 is a flow diagram generally illustrating an alternative methodfor providing, based on treatment data pertaining to a user who uses thetreatment device of FIG. 2, a recommendation to a healthcare provideraccording to the principles of the present disclosure.

FIG. 11 is a flow diagram generally illustrating an alternative methodfor providing, based on treatment data pertaining to a user who uses thetreatment device of FIG. 2, a recommendation to a healthcare provideraccording to the principles of the present disclosure.

FIG. 12 is a flow diagram generally illustrating a method for receivinga selection of an optimal treatment plan and controlling, based on theoptimal treatment plan, a treatment device while the patient uses thetreatment device according to the present disclosure.

FIG. 13 generally illustrates a computer system according to theprinciples of the present disclosure.

NOTATION AND NOMENCLATURE

Various terms are used to refer to particular system components.Different companies may refer to a component by different names—thisdocument does not intend to distinguish between components that differin name but not function. In the following discussion and in the claims,the terms “including” and “comprising” are used in an open-endedfashion, and thus should be interpreted to mean “including, but notlimited to . . . ” Also, the term “couple” or “couples” is intended tomean either an indirect or direct connection. Thus, if a first devicecouples to a second device, that connection may be through a directconnection or through an indirect connection via other devices andconnections.

The terminology used herein is for the purpose of describing particularexample embodiments only, and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. The method steps, processes, and operations described hereinare not to be construed as necessarily requiring their performance inthe particular order discussed or illustrated, unless specificallyidentified as an order of performance. It is also to be understood thatadditional or alternative steps may be employed.

The terms first, second, third, etc. may be used herein to describevarious elements, components, regions, layers and/or sections; however,these elements, components, regions, layers and/or sections should notbe limited by these terms. These terms may be only used to distinguishone element, component, region, layer, or section from another region,layer, or section. Terms such as “first,” “second,” and other numericalterms, when used herein, do not imply a sequence or order unless clearlyindicated by the context. Thus, a first element, component, region,layer, or section discussed below could be termed a second element,component, region, layer, or section without departing from theteachings of the example embodiments. The phrase “at least one of,” whenused with a list of items, means that different combinations of one ormore of the listed items may be used, and only one item in the list maybe needed. For example, “at least one of: A, B, and C” includes any ofthe following combinations: A, B, C, A and B, A and C, B and C, and Aand B and C. In another example, the phrase “one or more” when used witha list of items means there may be one item or any suitable number ofitems exceeding one.

Spatially relative terms, such as “inner,” “outer,” “beneath,” “below,”“lower,” “above,” “upper,” “top,” “bottom,” and the like, may be usedherein. These spatially relative terms can be used for ease ofdescription to describe one element's or feature's relationship toanother element(s) or feature(s) as illustrated in the figures. Thespatially relative terms may also be intended to encompass differentorientations of the device in use, or operation, in addition to theorientation depicted in the figures. For example, if the device in thefigures is turned over, elements described as “below” or “beneath” otherelements or features would then be oriented “above” the other elementsor features. Thus, the example term “below” can encompass both anorientation of above and below. The device may be otherwise oriented(rotated 90 degrees or at other orientations) and the spatially relativedescriptions used herein interpreted accordingly.

A “treatment plan” may include one or more treatment protocols, and eachtreatment protocol includes one or more treatment sessions. Eachtreatment session comprises several session periods, with each sessionperiod including a particular exercise for treating the body part of thepatient. For example, a treatment plan for post-operative rehabilitationafter a knee surgery may include an initial treatment protocol withtwice daily stretching sessions for the first 3 days after surgery and amore intensive treatment protocol with active exercise sessionsperformed 4 times per day starting 4 days after surgery. A treatmentplan may also include information pertaining to a medical procedure toperform on the patient, a treatment protocol for the patient using atreatment device, a diet regimen for the patient, a medication regimenfor the patient, a sleep regimen for the patient, additional regimens,or some combination thereof.

The terms telemedicine, telehealth, telemed, teletherapeutic,telemedicine, remote medicine, etc. may be used interchangeably herein.

The term “enhanced reality” may include a user experience comprising oneor more of augmented reality, virtual reality, mixed reality, immersivereality, or a combination of the foregoing (e.g., immersive augmentedreality, mixed augmented reality, virtual and augmented immersivereality, and the like).

The term “augmented reality” may refer, without limitation, to aninteractive user experience that provides an enhanced environment thatcombines elements of a real-world environment with computer-generatedcomponents perceivable by the user.

The term “virtual reality” may refer, without limitation, to a simulatedinteractive user experience that provides an enhanced environmentperceivable by the user and wherein such enhanced environment may besimilar to or different from a real-world environment.

The term “mixed reality” may refer to an interactive user experiencethat combines aspects of augmented reality with aspects of virtualreality to provide a mixed reality environment perceivable by the user.

The term “immersive reality” may refer to a simulated interactive userexperienced using virtual and/or augmented reality images, sounds, andother stimuli to immerse the user, to a specific extent possible (e.g.,partial immersion or total immersion), in the simulated interactiveexperience. For example, in some embodiments, to the specific extentpossible, the user experiences one or more aspects of the immersivereality as naturally as the user typically experiences correspondingaspects of the real-world. Additionally, or alternatively, an immersivereality experience may include actors, a narrative component, a theme(e.g., an entertainment theme or other suitable theme), and/or othersuitable features of components.

The term “body halo” may refer to a hardware component or components,wherein such component or components may include one or more platforms,one or more body supports or cages, one or more chairs or seats, one ormore back supports, one or more leg or foot engaging mechanisms, one ormore arm or hand engaging mechanisms, one or more neck or head engagingmechanisms, other suitable hardware components, or a combinationthereof.

As used herein, the term “enhanced environment” may refer to an enhancedenvironment in its entirety, at least one aspect of the enhancedenvironment, more than one aspect of the enhanced environment, or anysuitable number of aspects of the enhanced environment.

The term “medical action(s)” may refer to any suitable action performedby the medical professional (e.g., or the healthcare professional), andsuch action or actions may include diagnoses, prescription of treatmentplans, prescription of treatment devices, and the making, composingand/or executing of appointments, telemedicine sessions, prescriptionsor medicines, telephone calls, emails, text messages, and the like.

DETAILED DESCRIPTION

The following discussion is directed to various embodiments of thepresent disclosure. Although one or more of these embodiments may bepreferred, the embodiments disclosed should not be interpreted, orotherwise used, as limiting the scope of the disclosure, including theclaims. In addition, one skilled in the art will understand that thefollowing description has broad application, and the discussion of anyembodiment is meant only to be exemplary of that embodiment, and notintended to intimate that the scope of the disclosure, including theclaims, is limited to that embodiment.

Determining optimal remote examination procedures to create an optimaltreatment plan for a patient having certain characteristics (e.g.,vital-sign or other measurements; performance; demographic;psychographic; geographic; diagnostic; measurement- or test-based;medically historic; etiologic; cohort-associative; differentiallydiagnostic; surgical, physically therapeutic, behavioral, pharmacologicand other treatment(s) recommended; etc.) may be a technicallychallenging problem. For example, a multitude of information may beconsidered when determining a treatment plan, which may result ininefficiencies and inaccuracies in the treatment plan selection process.In a rehabilitative setting, some of the multitude of informationconsidered may include characteristics of the patient such as personalinformation, performance information, and measurement information. Thepersonal information may include, e.g., demographic, psychographic orother information, such as an age, a weight, a gender, a height, a bodymass index, a medical condition, a familial medication history, aninjury, a medical procedure, a medication prescribed, or somecombination thereof. The performance information may include, e.g., anelapsed time of using a treatment device, an amount of force exerted ona portion of the treatment device, a range of motion achieved on thetreatment device, a movement speed of a portion of the treatment device,a duration of use of the treatment device, an indication of a pluralityof pain levels using the treatment device, or some combination thereof.The measurement information may include, e.g., a vital sign, arespiration rate, a heartrate, a temperature, a blood pressure, aglucose level or other biomarker, microbiome information, or somecombination thereof. It may be desirable to process and analyze thecharacteristics of a multitude of patients, the treatment plansperformed for those patients, and the results of the treatment plans forthose patients.

Further, another technical problem may involve distally treating, via acomputing device during a telemedicine or telehealth session, a patientfrom a location different than a location at which the patient islocated. An additional technical problem is controlling or enabling thecontrol of, from the different location, a treatment device used by thepatient at the location at which the patient is located. Oftentimes,when a patient undergoes rehabilitative surgery (e.g., knee surgery), ahealthcare provider may prescribe a treatment device to the patient touse to perform a treatment protocol at their residence or any mobilelocation or temporary domicile. A healthcare provider may refer to adoctor, physician assistant, nurse, chiropractor, dentist, physicaltherapist, acupuncturist, physical trainer, coach, personal trainer,neurologist, cardiologist, or the like. A healthcare provider may referto any person with a credential, license, degree, or the like in thefield of medicine, physical therapy, rehabilitation, or the like.

When the healthcare provider is located in a different location from thepatient and the treatment device, it may be technically challenging forthe healthcare provider to monitor the patient's actual progress (asopposed to relying on the patient's word about their progress) using thetreatment device, modify the treatment plan according to the patient'sprogress, adapt the treatment device to the personal characteristics ofthe patient as the patient performs the treatment plan, and the like.

Typically, the healthcare provider may schedule one or more appointmentsto review the patient's progress, to perform one or more examinationprocedures, and/or the like. The healthcare provider may schedule suchappointments arbitrarily, according to a standard appointment scheduleor cadence, For example, the healthcare provider may schedule suchappointments based on scheduling availability, based on a set cadence(e.g., every 6 weeks, or the like), or other similar arbitrary- orstandards-based scheduling practices.

However, such schedule practices may be suboptimal in view of one ormore of the following aspects of the patient's actual progress: the costof treatment; allocations and/or amounts of and/or timing of insurancereimbursements; known treatment protocols; data from evidence-basedmedicine; actuarial data; optimizing the treatment of more than onepatient by best allocating limited physician, healthcare professional,hospital and other medical resource allocations to the more than onepatient so as to optimize the patient outcomes of the one or morepatients as measured by statistical measures such as mean, median, ormode; and the like. For example, during a first appointment, thehealthcare provider may add or modify at least one aspect of thetreatment plan. The healthcare provider may schedule a secondappointment a period (e.g., a number of days or weeks) after the firstappointment. The new or modified at least one aspect of the treatmentplan may require the patient to perform, for example, a new, modified ordifferent treatment protocol.

During the period between the first appointment and the secondappointment, the patient may perform the new or modified aspect of thetreatment plan. If, during the period, the patient is not performing thenew or modified aspect of the treatment plan, if the patient is notbenefiting from the new or modified aspect of the treatment plan, if thepatient is not progressing according to expectations, or if the patientis progressing above expectations, the patient may benefit from thesecond appointment being scheduled at a different time (e.g., closer intime to the first appointment or further in time from the firstappointment).

Accordingly, systems and methods, such as those described herein, thatprovide, to a healthcare provider, one or more recommendations using, atleast, treatment data pertaining to a user who uses the treatment deviceto perform a treatment plan, may be desirable.

In some embodiments, the systems and methods described herein may beconfigured to receive treatment data pertaining to a user who uses atreatment device to perform a treatment plan. The user may include apatient, user, or person using the treatment device to perform variousexercises. The treatment data may include various characteristics of theuser, various measurement information pertaining to the user while theuser uses the treatment device, various characteristics of the treatmentdevice, the treatment plan, other suitable data, or a combinationthereof. In some embodiments, the systems and methods described hereinmay be configured to receive the treatment data during a telemedicinesession.

In some embodiments, while the user uses the treatment device to performthe treatment plan, at least some of the treatment data may correspondto sensor data of a sensor configured to sense various characteristicsof the treatment device and/or to obtain the measurement informationfrom the user. Additionally, or alternatively, while the user uses thetreatment device to perform the treatment plan, at least some of thetreatment data may correspond to sensor data from a sensor associatedwith a wearable device configured to measure, determine, or obtain themeasurement information associated with the user.

The various characteristics of the treatment device may include one ormore settings of the treatment device, a current revolutions per timeperiod (e.g., such as one minute) of a rotating member (e.g., such as awheel) of the treatment device, a resistance setting of the treatmentdevice, other suitable characteristics of the treatment device, or acombination thereof. The measurement information may include one or morevital signs of the user, a respiration rate of the user, a heartrate ofthe user, a temperature of the user, a blood pressure of the user, ablood oxygen level (e.g., SpO2) of the user, a glucose level of theuser, other suitable measurement information of the user,microbiome-related data pertaining to the user, or a combinationthereof.

Additionally, or alternatively, the treatment data may include variousaspects of the treatment plan, including one or more treatmentschedules, one or more appointments, other suitable aspects of thetreatment plan, or a combination thereof. A treatment schedule maydefine a cadence, interval, or frequency for the user to perform variousaspects of the treatment plan. For example, a treatment scheduleassociated with the treatment plan may define, for a period, a number oftreatment sessions during which the user is indicated to perform one ormore exercises defined by the treatment plan.

An appointment associated with the treatment plan may define a day andtime when the healthcare provider will assess or schedule an assessmentof the user's progress, perform one or more examination procedures onthe user, evaluate and/or modify the various aspects of the treatmentplan, evaluate and/or modify various characteristics of the treatmentdevice, and the like.

It is appreciated that the healthcare provider may not be on time forthe appointment associated with the treatment plan and, therefore, anyappointment occurring approximately before or after the time for theappointment associated with the treatment plan and consistent withtypical healthcare provider practices shall, for the purposes hereof,still be deemed an appointment associated with the treatment plan. It isfurther understood that certain types of healthcare providers mayslightly deviate from the appointment associated with the treatment planwhile others (e.g., an obstetrician who must attend to a birth, asurgeon who must attend to emergency surgery, and the like) may deviatefurther from the appointment associated with the treatment plan or evenreschedule the appointment associated with the treatment plan. In allsuch cases, for the purposes hereof, the actual appointment associatedwith the treatment plan shall still be deemed the appointment associatedwith the treatment plan. The appointment may be recurring (e.g., on aperiodic basis) or non-recurring. It should be further understood thatthe healthcare provider may assess the user's progress, perform one ormore examination procedures on the user, evaluate and/or modify thevarious aspects of the treatment plan, evaluate and/or modify variouscharacteristics of the treatment device, and the like during anappointment, between appointments, and/or at any suitable time.

In some embodiments, the systems and methods described herein may beconfigured to write the treatment data to an associated memory. Theassociated memory may include any suitable memory, such as thosedescribed herein. The associated memory may be configured to be accessedby an artificial intelligence engine. The artificial intelligence enginemay configured to use at least one machine learning model to generate,using the treatment data, at least one of a treatment scheduling outputprediction and an appointment output. The at least one machine learningmodel may include any suitable machine learning model, such as thosedescribed herein. For example, the at least one machine learning modelmay include a deep network comprising multiple levels of non-linearoperations or other suitable model.

In some embodiments, the treatment scheduling output predictiongenerated by the at least one machine learning model may indicate, atleast, a cadence, interval, or frequency for the user to perform variousaspects of the treatment plan. For example, the machine learning modelmay determine, based on the treatment data, that a predicted cadence,interval, or frequency for the user to perform the various aspects ofthe treatment device may yield one or more expected results, such as anexpected performance of the user, an expected progress of the user, andthe like. The treatment scheduling output prediction may include aprobabilistic prediction, a stochastic prediction, or a deterministicprediction.

In some embodiments, the appointment output may indicate a period forone or more future appointments or periods corresponding to respectivefuture appointments. For example, the machine learning model maydetermine, based on the treatment data, that the user may benefit from asingle future appointment set during a first period. The first periodmay include a timeframe (e.g., in days, weeks, months, and so on) forsetting the single future appointment. Additionally, or alternatively,the machine learning model may determine, based on the treatment data,that the user may benefit from two or more future appointments. Themachine learning model may indicate, in the appointment output, a firstperiod for setting a first appointment of the two or more appointments,a second period for setting a second of the two or more appointments,and so on. Each period indicated by the machine learning model mayinclude a corresponding timeframe (e.g., in days, weeks, months, and soon). The timeframe for each respective period may be the same ordifferent.

In some embodiments, the systems and methods described herein may beconfigured to receive, from the artificial intelligence engine, the atleast one of the treatment scheduling output prediction and theappointment output. In some embodiments, the systems and methodsdescribed herein may be configured to selectively modify, using the atleast one of the treatment scheduling output prediction and theappointment output, one or more aspects of the treatment plan.

In some embodiments, the systems and methods described herein may beconfigured to receive other treatment data pertaining to at least oneother user who uses at least one of the treatment device or anothertreatment device to perform another treatment plan. The at least oneother user may include one or more other patients, users, or personsusing the treatment device or other corresponding treatment devices toperform various exercises. The other treatment data may include variouscharacteristics of the at least one other user, various measurementinformation pertaining to the at least one other user while the at leastone other user uses the treatment device or another treatment device,various characteristics of the treatment device or the other treatmentdevice, the other treatment plan, other suitable data, or a combinationthereof. In some embodiments, the systems and methods described hereinmay be configured to receive the other treatment data during acorresponding telemedicine session.

In some embodiments, while the at least one other user uses thetreatment device or another treatment device to perform the othertreatment plan, at least some of the other treatment data may correspondto sensor data of a sensor configured to sense various characteristicsof the treatment device or another treatment device and/or themeasurement information of the at least one other user. Additionally, oralternatively, while the at least one other user uses the treatmentdevice or other treatment to perform the other treatment plan, at leastsome of the other treatment data may correspond to sensor data from asensor associated with a wearable device configured to sense and/orobtain the measurement information of the at least one other user.

In some embodiments, the systems and methods described herein may beconfigured to determine, using the artificial intelligence engine thatuses the at least one machine learning model, whether to group the userwith the at least one other user. In some embodiments, the systems andmethods described herein may determine, using the artificialintelligence engine that uses the at least one machine learning model,that the treatment data and the other treatment data share at least somesimilar aspects. In response to a determination that the treatment dataand the other treatment data share at least some similar aspects, thesystems and methods described herein may be configured to determine thatgrouping the user and the at least one other user may be beneficial toat least one of the user and the other user.

In some embodiments, in response to the systems and methods describedherein determining to group the user with the at least one other user,the systems and methods described herein may group the user with the atleast one other user by at least associating (e.g., coordinating,synchronizing, and the like) the at least one of the treatment scheduleand the at least one appointment of the treatment plan (e.g., pertainingto the user) with the at least one of the treatment schedule and the atleast one appointment of the other treatment plan (e.g., pertaining tothe at least one other user).

In some embodiments, associating the at least one of the treatmentschedule and the at least one appointment of the treatment plan (e.g.,pertaining to the user) with the at least one of the treatment scheduleand the at least one appointment of the other treatment plan (e.g.,pertaining to the at least one other user) may include making thetreatment schedule of the treatment plan identical or substantiallysimilar to the treatment schedule of the other treatment plan, settingthe treatment schedule of the treatment plan on to follow a cadence,interval, or frequency that alternates or otherwise corresponds to acadence, interval, or frequency of the treatment schedule of the othertreatment plan, setting a first appointment or one or more appointmentsof the at least one appointment of the treatment plan at the same timeas a first appointment or two or more appointments of the at least oneappointment of the other treatment plan, setting a first appointment ortwo or more appointments of the at least one appointment of the othertreatment plan according to a first appointment or two or moreappointments of the treatment plan (e.g. at different times butaccording to the same or similar period or discrete time interval, suchas later in time but at the same number of weeks between appointments,for example), other suitable association of the at least one of thetreatment schedule and the at least one appointment of the treatmentplan (e.g., pertaining to the user) with the at least one of thetreatment schedule and the at least one appointment of the othertreatment plan (e.g., pertaining to the at least one other user), or acombination thereof.

In some embodiments, the systems and methods described herein may beconfigured to group the user and/or the other user with any suitablenumber of other users. Additionally, or alternatively, the treatmentschedule of the treatment plan and/or the at least one appointment ofthe treatment plan may precede other treatment schedules and/or at leastone other appointment of other respective treatment plans. The systemsand methods described herein may be configured to use the treatmentschedule and/or the at least one appointment of the treatment plan as amodel for other treatment schedules and/or at least one otherappointment of other respective treatment plans (e.g., the othertreatment schedules and/or at least one other appointment of otherrespective treatment plans may follow the same or similar schedule asthe treatment schedule of the treatment plan and/or the same or similarperiods for the at least one other appointment of other respectivetreatment plans). In some embodiments, the systems and methods describedherein may be configured to generate treatment information using thetreatment data. The treatment information may include a summary of theperformance, by the user while using the treatment device, of thetreatment plan formatted, such that the treatment data is presentable ata computing device of a healthcare provider responsible for theperformance of the treatment plan by the user. The treatment data may bepresented to the user via the user's computing device, which may enablethe user to better understand their progress, performance, and futuregoals. Further, presenting the treatment data to the user may motivatethe user to continue to perform the treatment plan. In some embodiments,presenting the treatment data to the user may specify a problem of thetreatment plan and/or non-compliance with the treatment plan, which maybe subsequently addressed. The healthcare provider may include a medicalprofessional (e.g., such as a doctor, a nurse, a therapist, and thelike), an exercise professional (e.g., such as a coach, a trainer, anutritionist, and the like), or another professional sharing at leastone of medical and exercise attributes (e.g., such as an exercisephysiologist, a physical therapist, an occupational therapist, and thelike). As used herein, and without limiting the foregoing, a “healthcareprovider” may be a human being, a robot, a virtual assistant, a virtualassistant in virtual and/or augmented reality, or an artificiallyintelligent entity, such entity including a software program, integratedsoftware and hardware, or hardware alone.

The systems and methods described herein may be configured to write toan associated memory, for access at the computing device of thehealthcare provider and/or the user. The systems and methods mayprovide, at the computing device of the healthcare provider and/or theuser, the treatment information. For example, the systems and methodsdescribe herein may be configured to provide the treatment informationto an interface configured to present the treatment information to thehealthcare provider. The interface may include a graphical userinterface configured to provide the treatment information and receiveinput from the healthcare provider. The interface may include one ormore input fields, such as text input fields, dropdown selection inputfields, radio button input fields, virtual switch input fields, virtuallever input fields, audio, haptic, tactile, biometric, and/or gesturerecognition, gesture control, touchless user interfaces (TUIs), kineticuser interfaces (KUIs), tangible user interfaces, wired gloves,depth-aware cameras, stereo cameras, gesture-based controllers, orotherwise activated and/or driven input fields, other suitable inputfields, or a combination thereof.

In some embodiments, the healthcare provider may review the treatmentinformation and determine whether to modify at least one aspect of thetreatment plan, and/or one or more characteristics of the treatmentdevice. For example, the healthcare provider may review the treatmentinformation and compare the treatment information to the treatment planbeing performed by the user.

The healthcare provider may compare the following (i) expectedinformation, which pertains to the user's expected or predictedperformance when the user actually uses the treatment device to performthe treatment plan to (ii) the measurement information (e.g., indicatedby the treatment information), which pertains to the user while the useris using the treatment device to perform the treatment plan.

The expected information may include one or more vital signs of theuser, a respiration rate of the user, a heartrate of the user, atemperature of the user, a blood pressure of the user, a blood oxygenlevel (e.g., SpO2) of the user, a glucose level of the user, othersuitable measurement information from the user, microbiome related datapertaining to the user, or a combination thereof, a blood oxygen level(e.g., SpO2) of the user. The healthcare provider may determine that thetreatment plan is having the desired effect if one or more parts orportions of the measurement information are within an acceptable rangeassociated with one or more corresponding parts or portions of theexpected information. Alternatively, the healthcare provider maydetermine that the treatment plan is not having the desired effect(e.g., not achieving the desired effect or a portion of the desiredeffect) if one or more parts or portions of the measurement informationare outside of the range associated with one or more corresponding partsor portions of the expected information.

For example, the healthcare provider may determine whether a bloodpressure value (e.g., systolic pressure, diastolic pressure, and/orpulse pressure) corresponding to the user while the user uses thetreatment device (e.g., indicated by the measurement information) iswithin an acceptable range (e.g., plus or minus 1%, plus or minus 5%,plus or minus a particular number of units suitable for the measurement(e.g., actual or digitally equivalent column inches of mercury for bloodpressure, and the like), or any suitable range) of an expected bloodpressure value indicated by the expected information. The healthcareprovider may determine that the treatment plan is having the desiredeffect (e.g., achieving the desired effect or a portion of the desiredeffect) if the blood pressure value corresponding to the user while theuser uses the treatment device is within the range of the expected bloodpressure value. Alternatively, the healthcare provider may determinethat the treatment plan is not having the desired effect if the bloodpressure value corresponding to the user while the user uses thetreatment device is outside of the range of the expected blood pressurevalue.

In some embodiments, the healthcare provider may compare the expectedcharacteristics of the treatment device while the user uses thetreatment device to perform the treatment plan with characteristics ofthe treatment device indicated by the treatment information. Forexample, the healthcare provider may compare an expected resistancesetting of the treatment device with an actual resistance setting of thetreatment device indicated by the treatment information. The healthcareprovider may determine that the user is performing the treatment planproperly if the actual characteristics of the treatment device indicatedby the treatment information are within a range of corresponding ones ofthe expected characteristics of the treatment device. Alternatively, thehealthcare provider may determine that the user is not performing thetreatment plan properly if the actual characteristics of the treatmentdevice indicated by the treatment information are outside the range ofcorresponding ones of the expected characteristics of the treatmentdevice.

If the healthcare provider determines that the treatment informationindicates that the user is performing the treatment plan properly and/orthat the treatment plan is having the desired effect, the healthcareprovider may determine not to modify the at least one aspect of thetreatment plan, and/or the one or more characteristics of the treatmentdevice. Alternatively, while the user uses the treatment device toperform the treatment plan, if the healthcare provider determines thatthe treatment information indicates that the user is not or has not beenperforming the treatment plan properly and/or that the treatment plan isnot or has not been having the desired effect, the healthcare providermay determine to modify the at least one aspect of the treatment planand/or the one or more characteristics of the treatment device.

In some embodiments, the healthcare provider may interact with theinterface to provide treatment plan input indicating one or moremodifications to the treatment plan, and/or one or more characteristicsof the treatment device if the healthcare provider determines to modifythe treatment plan, and/or to one or more characteristics of thetreatment device. For example, the healthcare provider may use theinterface to provide input indicating an increase or decrease in theresistance setting of the treatment device, or other suitablemodification to the one or more characteristics of the treatment device.Additionally, or alternatively, the healthcare provider may use theinterface to provide input indicating a modification to the treatmentplan. For example, the healthcare provider may use the interface toprovide input indicating an increase or decrease in an amount of timethe user is required to use the treatment device according to thetreatment plan, or other suitable modifications to the treatment plan.

In some embodiments, the systems and methods described herein may beconfigured to modify the treatment plan based on one or moremodifications indicated by the treatment plan input. Additionally, oralternatively, the systems and methods described herein may beconfigured to modify the one or more characteristics of the treatmentdevice based on the modified treatment plan and/or the treatment planinput. For example, the treatment plan input may indicate that the oneor more characteristics of the treatment device should be modifiedand/or the modified treatment plan may require or indicate adjustmentsto the treatment device in order for the user to achieve the desiredresults of the modified treatment plan.

In some embodiments, while the user uses the treatment device to performthe modified treatment plan, the systems and methods described hereinmay be configured to receive the subsequent treatment data pertaining tothe user. For example, after the healthcare provider provides inputmodifying the treatment plan and/or controlling the one or morecharacteristics of the treatment device, the user may continue using thetreatment device to perform the modified treatment plan. The subsequenttreatment data may correspond to treatment data generated while the useruses the treatment device to perform the modified treatment plan. Insome embodiments, the subsequent treatment data may correspond totreatment data generated while the user continues to use the treatmentdevice to perform the treatment plan, after the healthcare provider hasreceived the treatment information and determined not to modify thetreatment plan and/or control the one or more characteristics of thetreatment device.

Based on subsequent treatment plan input received from the computingdevice of the healthcare provider, the systems and methods describedherein may be configured to further modify the treatment plan, and/or tofurther control the one or more characteristics of the treatment device.The subsequent treatment plan input may correspond to input provided bythe healthcare provider, at the interface, in response to receivingand/or reviewing subsequent treatment information corresponding to thesubsequent treatment data. It should be understood that the systems andmethods described herein may be configured to continuously and/orperiodically provide treatment information to the computing device ofthe healthcare provider based on treatment data continuously and/orperiodically received from the sensors or other suitable sourcesdescribed herein.

The healthcare provider may receive and/or review treatment informationcontinuously or periodically while the user uses the treatment device toperform the treatment plan. Based on one or more trends indicated by thecontinuously and/or periodically received treatment information, thehealthcare provider may determine whether to modify the treatment plan,and/or to control the one or more characteristics of the treatmentdevice. For example, the one or more trends may indicate an increase inheartrate or other suitable trends indicating that the user is notperforming the treatment plan properly and/or that performance of thetreatment plan by the user is not having the desired effect.

In some embodiments, during an adaptive telemedicine session, thesystems and methods described herein may be configured to use artificialintelligence and/or machine learning to assign patients to cohorts andto dynamically control a treatment device based on the assignment. Theterm “adaptive telemedicine” may refer to a telemedicine session that isdynamically adapted based on one or more factors, criteria, parameters,characteristics, or the like. The one or more factors, criteria,parameters, characteristics, or the like may pertain to the user (e.g.,heartrate, blood pressure, perspiration rate, pain level, or the like),the treatment device (e.g., pressure, range of motion, speed of motor,etc.), details of the treatment plan, and so forth.

In some embodiments, numerous patients may be prescribed numeroustreatment devices because the numerous patients are recovering from thesame medical procedure and/or suffering from the same injury. Thenumerous treatment devices may be provided to the numerous patients. Thetreatment devices may be used by the patients to perform treatment plansin their residences, at gyms, at rehabilitative centers, at hospitals,or at any suitable locations, including permanent or temporarydomiciles.

In some embodiments, the treatment devices may be communicativelycoupled to a server. Characteristics of the patients, including thetreatment data, may be collected before, during, and/or after thepatients perform the treatment plans. For example, any or each of thepersonal information, the performance information, and the measurementinformation may be collected before, during, and/or after a patientperforms the treatment plans. The results (e.g., improved performance ordecreased performance) of performing each exercise may be collected fromthe treatment device throughout the treatment plan and after thetreatment plan is performed. The parameters, settings, configurations,etc. (e.g., position of pedal, amount of resistance, etc.) of thetreatment device may be collected before, during, and/or after thetreatment plan is performed.

Each characteristic of the patient, each result, and each parameter,setting, configuration, etc. may be timestamped and may be correlatedwith a particular step or set of steps in the treatment plan. Such atechnique may enable the determination of which steps in the treatmentplan lead to desired results (e.g., improved muscle strength, range ofmotion, etc.) and which steps lead to diminishing returns (e.g.,continuing to exercise after 3 minutes actually delays or harmsrecovery).

Data may be collected from the treatment devices and/or any suitablecomputing device (e.g., computing devices where personal information isentered, such as the interface of the computing device described herein,a clinician interface, patient interface, and the like) over time as thepatients use the treatment devices to perform the various treatmentplans. The data that may be collected may include the characteristics ofthe patients, the treatment plans performed by the patients, the resultsof the treatment plans, any of the data described herein, any othersuitable data, or a combination thereof.

In some embodiments, the data may be processed to group certain peopleinto cohorts. The people may be grouped by people having certain orselected similar characteristics, treatment plans, and results ofperforming the treatment plans. For example, athletic people having nomedical conditions who perform a treatment plan (e.g., use the treatmentdevice for 30 minutes a day 5 times a week for 3 weeks) and who fullyrecover may be grouped into a first cohort. Older people who areclassified obese and who perform a treatment plan (e.g., use thetreatment plan for 10 minutes a day 3 times a week for 4 weeks) and whoimprove their range of motion by 75 percent may be grouped into a secondcohort.

In some embodiments, an artificial intelligence engine may include oneor more machine learning models that are trained using the cohorts. Insome embodiments, the artificial intelligence engine may be used toidentify trends and/or patterns and to define new cohorts based onachieving desired results from the treatment plans and machine learningmodels associated therewith may be trained to identify such trendsand/or patterns and to recommend and rank the desirability of the newcohorts. For example, the one or more machine learning models may betrained to receive an input of characteristics of a new patient and tooutput a treatment plan for the patient that results in a desiredresult. The machine learning models may match a pattern between thecharacteristics of the new patient and at least one patient of thepatients included in a particular cohort. When a pattern is matched, themachine learning models may assign the new patient to the particularcohort and select the treatment plan associated with the at least onepatient. The artificial intelligence engine may be configured tocontrol, distally and based on the treatment plan, the treatment devicewhile the new patient uses the treatment device to perform the treatmentplan.

As may be appreciated, the characteristics of the new patient (e.g., anew user) may change as the new patient uses the treatment device toperform the treatment plan. For example, the performance of the patientmay improve quicker than expected for people in the cohort to which thenew patient is currently assigned. Accordingly, the machine learningmodels may be trained to dynamically reassign, based on the changedcharacteristics, the new patient to a different cohort that includespeople having characteristics similar to the now-changed characteristicsas the new patient. For example, a clinically obese patient may loseweight and no longer meet the weight criterion for the initial cohort,result in the patient's being reassigned to a different cohort with adifferent weight criterion.

A different treatment plan may be selected for the new patient, and thetreatment device may be controlled, distally (e.g., which may bereferred to as remotely) and based on the different treatment plan,while the new patient uses the treatment device to perform the treatmentplan. Such techniques may provide the technical solution of distallycontrolling a treatment device.

Further, the systems and methods described herein may lead to fasterrecovery times and/or better results for the patients because thetreatment plan that most accurately fits their characteristics isselected and implemented, in real-time, at any given moment. “Real-time”may also refer to near real-time, which may be less than 10 seconds. Asdescribed herein, the term “results” may refer to medical results ormedical outcomes. Results and outcomes may refer to responses to medicalactions. The term “medical action(s)” may refer to any suitable actionperformed by the healthcare provider, such actions may includediagnoses, prescription of treatment plans, prescription of treatmentdevices, and the making, composing and/or executing of appointments,telemedicine sessions, prescription of medicines, telephone calls,emails, text messages, and the like.

Depending on what result is desired, the artificial intelligence enginemay be trained to output several treatment plans. For example, oneresult may include recovering to a threshold level (e.g., 75% range ofmotion) in a fastest amount of time, while another result may includefully recovering (e.g., 100% range of motion) regardless of the amountof time. The data obtained from the patients and sorted into cohorts mayindicate that a first treatment plan provides the first result forpeople with characteristics similar to the patient's, and that a secondtreatment plan provides the second result for people withcharacteristics similar to the patient.

Further, the artificial intelligence engine may be trained to outputtreatment plans that are not optimal i.e., sub-optimal, nonstandard, orotherwise excluded (all referred to, without limitation, as “excludedtreatment plans”) for the patient. For example, if a patient has highblood pressure, a particular exercise may not be approved or suitablefor the patient as it may put the patient at unnecessary risk or eveninduce a hypertensive crisis and, accordingly, that exercise may beflagged in the excluded treatment plan for the patient. In someembodiments, the artificial intelligence engine may monitor thetreatment data received while the patient (e.g., the user) with, forexample, high blood pressure, uses the treatment device to perform anappropriate treatment plan and may modify the appropriate treatment planto include features of an excluded treatment plan that may providebeneficial results for the patient if the treatment data indicates thepatient is handling the appropriate treatment plan without aggravating,for example, the high blood pressure condition of the patient. In someembodiments, the artificial intelligence engine may modify the treatmentplan if the monitored data shows the plan to be inappropriate orcounterproductive for the user.

In some embodiments, the treatment plans and/or excluded treatment plansmay be presented, during a telemedicine or telehealth session, to ahealthcare provider. The healthcare provider may select a particulartreatment plan for the patient to cause that treatment plan to betransmitted to the patient and/or to control, based on the treatmentplan, the treatment device. In some embodiments, to facilitatetelehealth or telemedicine applications, including remote diagnoses,determination of treatment plans and rehabilitative and/or pharmacologicprescriptions, the artificial intelligence engine may receive and/oroperate distally from the patient and the treatment device.

In such cases, the recommended treatment plans and/or excluded treatmentplans may be presented simultaneously with a video of the patient inreal-time or near real-time during a telemedicine or telehealth sessionon a user interface of a computing device of a healthcare provider. Thevideo may also be accompanied by audio, text and other multimediainformation. Real-time may refer to less than or equal to 2 seconds.Real-time may also refer to near real-time, which may be less than 10seconds or any reasonably proximate difference between two differenttimes. Additionally, or alternatively, near real-time may refer to anyinteraction of a sufficiently short time to enable two individuals toengage in a dialogue via such user interface and will generally be lessthan 10 seconds but greater than 2 seconds.

Presenting the treatment plans generated by the artificial intelligenceengine concurrently with a presentation of the patient video may providean enhanced user interface because the healthcare provider may continueto visually and/or otherwise communicate with the patient while alsoreviewing the treatment plans on the same user interface. The enhanceduser interface may improve the healthcare provider's experience usingthe computing device and may encourage the healthcare provider to reusethe user interface. Such a technique may also reduce computing resources(e.g., processing, memory, network) because the healthcare provider doesnot have to switch to another user interface screen to enter a query fora treatment plan to recommend based on the characteristics of thepatient. The artificial intelligence engine may be configured toprovide, dynamically on the fly, the treatment plans and excludedtreatment plans.

In some embodiments, the treatment device may be adaptive and/orpersonalized because its properties, configurations, and positions maybe adapted to the needs of a particular patient. For example, the pedalsmay be dynamically adjusted on the fly (e.g., via a telemedicine sessionor based on programmed configurations in response to certainmeasurements being detected) to increase or decrease a range of motionto comply with a treatment plan designed for the user. In someembodiments, a healthcare provider may adapt, remotely during atelemedicine session, the treatment device to the needs of the patientby causing a control instruction to be transmitted from a server totreatment device. Such adaptive nature may improve the results ofrecovery for a patient, furthering the goals of personalized medicine,and enabling personalization of the treatment plan on a per-individualbasis.

A technical problem may occur which relates to the informationpertaining to the patient's medical condition being received indisparate formats. For example, a server may receive the informationpertaining to a medical condition of the patient from one or moresources (e.g., from an electronic medical record (EMR) system,application programming interface (API), or any suitable system that hasinformation pertaining to the medical condition of the patient). Thatis, some sources used by various healthcare providers may be installedon their local computing devices and may use proprietary formats.Accordingly, some embodiments of the present disclosure may use an APIto obtain, via interfaces exposed by APIs used by the sources, theformats used by the sources. In some embodiments, when information isreceived from the sources, the API may map, translate and/or convert theformat used by the sources to a standardized format used by theartificial intelligence engine. Further, the information mapped,translated and/or converted to the standardized format used by theartificial intelligence engine may be stored in a database accessed bythe artificial intelligence engine when performing any of the techniquesdisclosed herein. Using the information mapped, translated and/orconverted to a standardized format may enable the more accuratedetermination of the procedures to perform for the patient and/or abilling sequence.

To that end, the standardized information may enable the generation oftreatment plans and/or billing sequences having a particular formatconfigured to be processed by various applications (e.g., telehealth).For example, applications, such as telehealth applications, may beexecuting on various computing devices of medical professionals and/orpatients. The applications (e.g., standalone or web-based) may beprovided by a server and may be configured to process data according toa format in which the treatment plans are implemented. Accordingly, thedisclosed embodiments may provide a technical solution by (i) receiving,from various sources (e.g., EMR systems), information innon-standardized and/or different formats; (ii) standardizing theinformation; and (iii) generating, based on the standardizedinformation, treatment plans having standardized formats capable ofbeing processed by applications (e.g., telehealth applications)executing on computing devices of medical professional and/or patients.

FIG. 1 generally illustrates a block diagram of a computer-implementedsystem 10, hereinafter called “the system” for managing a treatmentplan. Managing the treatment plan may include using an artificialintelligence engine to recommend treatment plans and/or provide excludedtreatment plans that should not be recommended to a patient.

The system 10 also includes a server 30 configured to store (e.g., writeto an associated memory) and to provide data related to managing thetreatment plan. The server 30 may include one or more computers and maytake the form of a distributed and/or virtualized computer or computers.The server 30 also includes a first communication interface 32configured to communicate with the clinician interface 20 via a firstnetwork 34. In some embodiments, the first network 34 may include wiredand/or wireless network connections such as Wi-Fi, Bluetooth, ZigBee,Near-Field Communications (NFC), cellular data network, etc. The server30 includes a first processor 36 and a first machine-readable storagememory 38, which may be called a “memory” for short, holding firstinstructions 40 for performing the various actions of the server 30 forexecution by the first processor 36.

The server 30 is configured to store data regarding the treatment plan.For example, the memory 38 includes a system data store 42 configured tohold system data, such as data pertaining to treatment plans fortreating one or more patients. The server 30 is also configured to storedata regarding performance by a patient in following a treatment plan.For example, the memory 38 includes a patient data store 44 configuredto hold patient data, such as data pertaining to the one or morepatients, including data representing each patient's performance withinthe treatment plan.

Additionally, or alternatively, the characteristics (e.g., personal,performance, measurement, etc.) of the people, the treatment plansfollowed by the people, the level of compliance with the treatmentplans, and the results of the treatment plans may use correlations andother statistical or probabilistic measures to enable the partitioningof or to partition the treatment plans into different patientcohort-equivalent databases in the patient data store 44. For example,the data for a first cohort of first patients having a first similarinjury, a first similar medical condition, a first similar medicalprocedure performed, a first treatment plan followed by the firstpatient, and a first result of the treatment plan may be stored in afirst patient database. The data for a second cohort of second patientshaving a second similar injury, a second similar medical condition, asecond similar medical procedure performed, a second treatment planfollowed by the second patient, and a second result of the treatmentplan may be stored in a second patient database. Any singlecharacteristic or any combination of characteristics may be used toseparate the cohorts of patients. In some embodiments, the differentcohorts of patients may be stored in different partitions or volumes ofthe same database. There is no specific limit to the number of differentcohorts of patients allowed, other than as limited by mathematicalcombinatoric and/or partition theory.

This characteristic data, treatment plan data, and results data may beobtained from numerous treatment devices and/or computing devices and/ordigital storage media over time and stored in the data store 44. Thecharacteristic data, treatment plan data, and results data may becorrelated in the patient-cohort databases in the patient data store 44.The characteristics of the people may include personal information,performance information, and/or measurement information.

In addition to the historical information about other people stored inthe patient cohort-equivalent databases, real-time or near-real-timeinformation based on the current patient's characteristics about acurrent patient being treated may be stored in an appropriate patientcohort-equivalent database. The characteristics of the patient may bedetermined to match or be similar to the characteristics of anotherperson in a particular cohort (e.g., cohort A) and the patient may beassigned to that cohort.

In some embodiments, the server 30 may execute an artificialintelligence (AI) engine 11 that uses one or more machine learningmodels 13 to perform at least one of the embodiments disclosed herein.The server 30 may include a training engine 9 capable of generating theone or more machine learning models 13. The machine learning models 13may be trained to assign people to certain cohorts based on theircharacteristics, select treatment plans using real-time and historicaldata correlations involving patient cohort-equivalents, and control atreatment device 70, among other things.

The one or more machine learning models 13 may be generated by thetraining engine 9 and may be implemented in computer instructionsexecutable by one or more processing devices of the training engine 9and/or the servers 30. To generate the one or more machine learningmodels 13, the training engine 9 may train the one or more machinelearning models 13. The one or more machine learning models 13 may beused by the artificial intelligence engine 11.

The training engine 9 may be a rackmount server, a router computer, apersonal computer, a portable digital assistant, a smartphone, a laptopcomputer, a tablet computer, a netbook, a desktop computer, an Internetof Things (IoT) device, any other suitable computing device, or acombination thereof. The training engine 9 may be cloud-based or areal-time software platform, and it may include privacy software orprotocols, and/or security software or protocols.

To train the one or more machine learning models 13, the training engine9 may use a training data set of a corpus of the characteristics of thepeople that used the treatment device 70 to perform treatment plans, thedetails (e.g., treatment protocol including exercises, amount of time toperform the exercises, how often to perform the exercises, a schedule ofexercises, parameters/configurations/settings of the treatment device 70throughout each step of the treatment plan, etc.) of the treatment plansperformed by the people using the treatment device 70, and the resultsof the treatment plans performed by the people. The one or more machinelearning models 13 may be trained to match patterns of characteristicsof a patient with characteristics of other people assigned to aparticular cohort. The term “match” may refer to an exact match, acorrelative match, a substantial match, etc. The one or more machinelearning models 13 may be trained to receive the characteristics of apatient as input, map the characteristics to characteristics of peopleassigned to a cohort, and select a treatment plan from that cohort. Theone or more machine learning models 13 may also be trained to control,based on the treatment plan, the treatment device 70. The one or moremachine learning models 13 may also be trained to provide one or moretreatment plans options to a healthcare provider to select from tocontrol the treatment device 70.

Different machine learning models 13 may be trained to recommenddifferent treatment plans for different desired results. For example,one machine learning model may be trained to recommend treatment plansfor most effective recovery, while another machine learning model may betrained to recommend treatment plans based on speed of recovery.

Using training data that includes training inputs and correspondingtarget outputs, the one or more machine learning models 13 may refer tomodel artifacts created by the training engine 9. The training engine 9may find patterns in the training data wherein such patterns map thetraining input to the target output and generate the machine learningmodels 13 that capture these patterns. In some embodiments, theartificial intelligence engine 11, the database 33, and/or the trainingengine 9 may reside on another component (e.g., assistant interface 94,clinician interface 20, etc.) depicted in FIG. 1.

The one or more machine learning models 13 may comprise, e.g., a singlelevel of linear or non-linear operations (e.g., a support vector machine[SVM]) or the machine learning models 13 may be a deep network, i.e., amachine learning model comprising multiple levels of non-linearoperations. Examples of deep networks are neural networks includinggenerative adversarial networks, convolutional neural networks,recurrent neural networks with one or more hidden layers, and fullyconnected neural networks (e.g., each neuron may transmit its outputsignal to the input of the remaining neurons, as well as to itself). Forexample, the machine learning model may include numerous layers and/orhidden layers that perform calculations (e.g., dot products) usingvarious neurons.

The system 10 also includes a patient interface 50 configured tocommunicate information to a patient and to receive feedback from thepatient. Specifically, the patient interface includes an input device 52and an output device 54, which may be collectively called a patient userinterface 52, 54. The input device 52 may include one or more devices,such as a keyboard, a mouse, a touch screen input, a gesture sensor,and/or a microphone and processor configured for voice recognition. Theoutput device 54 may take one or more different forms including, forexample, a computer monitor or display screen on a tablet, smartphone,or a smart watch. The output device 54 may include other hardware and/orsoftware components such as a projector, virtual reality capability,augmented reality capability, etc. The output device 54 may incorporatevarious different visual, audio, or other presentation technologies. Forexample, the output device 54 may include a non-visual display, such asan audio signal, which may include spoken language and/or other soundssuch as tones, chimes, and/or melodies, which may signal differentconditions and/or directions. The output device 54 may comprise one ormore different display screens presenting various data and/or interfacesor controls for use by the patient. The output device 54 may includegraphics, which may be presented by a web-based interface and/or by acomputer program or application (App.). In some embodiments, the patientinterface 50 may include functionality provided by or similar toexisting voice-based assistants such as Siri by Apple, Alexa by Amazon,Google Assistant, or Bixby by Samsung.

As is generally illustrated in FIG. 1, the patient interface 50 includesa second communication interface 56, which may also be called a remotecommunication interface configured to communicate with the server 30and/or the clinician interface 20 via a second network 58. In someembodiments, the second network 58 may include a local area network(LAN), such as an Ethernet network. In some embodiments, the secondnetwork 58 may include the Internet, and communications between thepatient interface 50 and the server 30 and/or the clinician interface 20may be secured via encryption, such as, for example, by using a virtualprivate network (VPN). In some embodiments, the second network 58 mayinclude wired and/or wireless network connections such as Wi-Fi,Bluetooth, ZigBee, Near-Field Communications (NFC), cellular datanetwork, etc. In some embodiments, the second network 58 may be the sameas and/or operationally coupled to the first network 34.

The patient interface 50 includes a second processor 60 and a secondmachine-readable storage memory 62 holding second instructions 64 forexecution by the second processor 60 for performing various actions ofpatient interface 50. The second machine-readable storage memory 62 alsoincludes a local data store 66 configured to hold data, such as datapertaining to a treatment plan and/or patient data, such as datarepresenting a patient's performance within a treatment plan. Thepatient interface 50 also includes a local communication interface 68configured to communicate with various devices for use by the patient inthe vicinity of the patient interface 50. The local communicationinterface 68 may include wired and/or wireless communications. In someembodiments, the local communication interface 68 may include a localwireless network such as Wi-Fi, Bluetooth, ZigBee, Near-FieldCommunications (NFC), cellular data network, etc.

The system 10 also includes a treatment device 70 configured to bemanipulated by the patient and/or to manipulate a body part of thepatient for performing activities according to the treatment plan. Insome embodiments, the treatment device 70 may take the form of anexercise and rehabilitation apparatus configured to perform and/or toaid in the performance of a rehabilitation regimen, which may be anorthopedic rehabilitation regimen, and the treatment includesrehabilitation of a body part of the patient, such as a joint or a boneor a muscle group. The treatment device 70 may be any suitable medical,rehabilitative, therapeutic, etc. apparatus configured to be controlleddistally via another computing device to treat a patient and/or exercisethe patient. The treatment device 70 may be an electromechanical machineincluding one or more weights, an electromechanical bicycle, anelectromechanical spin-wheel, a smart-mirror, a treadmill, aninteractive environment system, or the like. The body part may include,for example, a spine, a hand, a foot, a knee, or a shoulder. The bodypart may include a part of a joint, a bone, or a muscle group, such asone or more vertebrae, a tendon, or a ligament. As is generallyillustrated in FIG. 1, the treatment device 70 includes a controller 72,which may include one or more processors, computer memory, and/or othercomponents. The treatment device 70 also includes a fourth communicationinterface 74 configured to communicate with the patient interface 50 viathe local communication interface 68. The treatment device 70 alsoincludes one or more internal sensors 76 and an actuator 78, such as amotor. The actuator 78 may be used, for example, for moving thepatient's body part and/or for resisting forces by the patient.

The internal sensors 76 may measure one or more operatingcharacteristics of the treatment device 70 such as, for example, aforce, a position, a speed, and/or a velocity. In some embodiments, theinternal sensors 76 may include a position sensor configured to measureat least one of a linear motion or an angular motion of a body part ofthe patient. For example, an internal sensor 76 in the form of aposition sensor may measure a distance that the patient is able to movea part of the treatment device 70, where such distance may correspond toa range of motion that the patient's body part is able to achieve. Insome embodiments, the internal sensors 76 may include a force sensorconfigured to measure a force applied by the patient. For example, aninternal sensor 76 in the form of a force sensor may measure a force orweight the patient is able to apply, using a particular body part, tothe treatment device 70.

The system 10 generally illustrated in FIG. 1 also includes anambulation sensor 82, which communicates with the server 30 via thelocal communication interface 68 of the patient interface 50. Theambulation sensor 82 may track and store a number of steps taken by thepatient. In some embodiments, the ambulation sensor 82 may take the formof a wristband, wristwatch, or smart watch. In some embodiments, theambulation sensor 82 may be integrated within a phone, such as asmartphone.

The system 10 generally illustrated in FIG. 1 also includes a goniometer84, which communicates with the server 30 via the local communicationinterface 68 of the patient interface 50. The goniometer 84 measures anangle of the patient's body part. For example, the goniometer 84 maymeasure the angle of flex of a patient's knee or elbow or shoulder.

The system 10 generally illustrated in FIG. 1 also includes a pressuresensor 86, which communicates with the server 30 via the localcommunication interface 68 of the patient interface 50. The pressuresensor 86 measures an amount of pressure or weight applied by a bodypart of the patient. For example, pressure sensor 86 may measure anamount of force applied by a patient's foot when pedaling a stationarybike.

The system 10 generally illustrated in FIG. 1 also includes asupervisory interface 90 which may be similar or identical to theclinician interface 20. In some embodiments, the supervisory interface90 may have enhanced functionality beyond what is provided on theclinician interface 20. The supervisory interface 90 may be configuredfor use by a person having responsibility for the treatment plan, suchas an orthopedic surgeon.

The system 10 generally illustrated in FIG. 1 also includes a reportinginterface 92 which may be similar or identical to the clinicianinterface 20. In some embodiments, the reporting interface 92 may haveless functionality from what is provided on the clinician interface 20.For example, the reporting interface 92 may not have the ability tomodify a treatment plan. Such a reporting interface 92 may be used, forexample, by a biller to determine the use of the system 10 for billingpurposes. In another example, the reporting interface 92 may not havethe ability to display patient identifiable information, presenting onlypseudonymized data and/or anonymized data for certain data fieldsconcerning a data subject and/or for certain data fields concerning aquasi-identifier of the data subject. Such a reporting interface 92 maybe used, for example, by a researcher to determine various effects of atreatment plan on different patients.

The system 10 includes an assistant interface 94 for a healthcareprovider, such as those described herein, to remotely communicate withthe patient interface 50 and/or the treatment device 70. Such remotecommunications may enable the healthcare provider to provide assistanceor guidance to a patient using the system 10. More specifically, theassistant interface 94 is configured to communicate a telemedicinesignal 96, 97, 98 a, 98 b, 99 a, 99 b with the patient interface 50 viaa network connection such as, for example, via the first network 34and/or the second network 58.

The telemedicine signal 96, 97, 98 a, 98 b, 99 a, 99 b comprises one ofan audio signal 96, an audiovisual signal 97, an interface controlsignal 98 a for controlling a function of the patient interface 50, aninterface monitor signal 98 b for monitoring a status of the patientinterface 50, an apparatus control signal 99 a for changing an operatingparameter of the treatment device 70, and/or an apparatus monitor signal99 b for monitoring a status of the treatment device 70. In someembodiments, each of the control signals 98 a, 99 a may beunidirectional, conveying commands from the assistant interface 94 tothe patient interface 50. In some embodiments, in response tosuccessfully receiving a control signal 98 a, 99 a and/or to communicatesuccessful and/or unsuccessful implementation of the requested controlaction, an acknowledgement message may be sent from the patientinterface 50 to the assistant interface 94.

In some embodiments, each of the monitor signals 98 b, 99 b may beunidirectional, status-information commands from the patient interface50 to the assistant interface 94. In some embodiments, anacknowledgement message may be sent from the assistant interface 94 tothe patient interface 50 in response to successfully receiving one ofthe monitor signals 98 b, 99 b.

In some embodiments, the patient interface 50 may be configured as apass-through for the apparatus control signals 99 a and the apparatusmonitor signals 99 b between the treatment device 70 and one or moreother devices, such as the assistant interface 94 and/or the server 30.For example, the patient interface 50 may be configured to transmit anapparatus control signal 99 a in response to an apparatus control signal99 a within the telemedicine signal 96, 97, 98 a, 98 b, 99 a, 99 b fromthe assistant interface 94.

In some embodiments, the assistant interface 94 may be presented on ashared physical device as the clinician interface 20. For example, theclinician interface 20 may include one or more screens that implementthe assistant interface 94. Alternatively or additionally, the clinicianinterface 20 may include additional hardware components, such as a videocamera, a speaker, and/or a microphone, to implement aspects of theassistant interface 94.

In some embodiments, one or more portions of the telemedicine signal 96,97, 98 a, 98 b, 99 a, 99 b may be generated from a prerecorded source(e.g., an audio recording, a video recording, or an animation) forpresentation by the output device 54 of the patient interface 50. Forexample, a tutorial video may be streamed from the server 30 andpresented upon the patient interface 50. Content from the prerecordedsource may be requested by the patient via the patient interface 50.Alternatively, via a control on the assistant interface 94, thehealthcare provider may cause content from the prerecorded source to beplayed on the patient interface 50.

The assistant interface 94 includes an assistant input device 22 and anassistant display 24, which may be collectively called an assistant userinterface 22, 24. The assistant input device 22 may include one or moreof a telephone, a keyboard, a mouse, a trackpad, or a touch screen, forexample. Alternatively or additionally, the assistant input device 22may include one or more microphones. In some embodiments, the one ormore microphones may take the form of a telephone handset, headset, orwide-area microphone or microphones configured for the healthcareprovider to speak to a patient via the patient interface 50.

In some embodiments, assistant input device 22 may be configured toprovide voice-based functionalities, with hardware and/or softwareconfigured to interpret spoken instructions by the healthcare providerby using the one or more microphones. The assistant input device 22 mayinclude functionality provided by or similar to existing voice-basedassistants such as Siri by Apple, Alexa by Amazon, Google Assistant, orBixby by Samsung. The assistant input device 22 may include otherhardware and/or software components. The assistant input device 22 mayinclude one or more general purpose devices and/or special-purposedevices.

The assistant display 24 may take one or more different forms including,for example, a computer monitor or display screen on a tablet, asmartphone, or a smart watch. The assistant display 24 may include otherhardware and/or software components such as projectors, virtual realitycapabilities, or augmented reality capabilities, etc. The assistantdisplay 24 may incorporate various different visual, audio, or otherpresentation technologies. For example, the assistant display 24 mayinclude a non-visual display, such as an audio signal, which may includespoken language and/or other sounds such as tones, chimes, melodies,and/or compositions, which may signal different conditions and/ordirections. The assistant display 24 may comprise one or more differentdisplay screens presenting various data and/or interfaces or controlsfor use by the healthcare provider. The assistant display 24 may includegraphics, which may be presented by a web-based interface and/or by acomputer program or application (App.).

In some embodiments, the system 10 may provide computer translation oflanguage from the assistant interface 94 to the patient interface 50and/or vice-versa. The computer translation of language may includecomputer translation of spoken language and/or computer translation oftext. Additionally or alternatively, the system 10 may provide voicerecognition and/or spoken pronunciation of text. For example, the system10 may convert spoken words to printed text and/or the system 10 mayaudibly speak language from printed text. The system 10 may beconfigured to recognize spoken words by any or all of the patient, theclinician, and/or the healthcare provider. In some embodiments, thesystem 10 may be configured to recognize and react to spoken requests orcommands by the patient. For example, in response to a verbal command bythe patient (which may be given in any one of several differentlanguages), the system 10 may automatically initiate a telemedicinesession.

In some embodiments, the server 30 may generate aspects of the assistantdisplay 24 for presentation by the assistant interface 94. For example,the server 30 may include a web server configured to generate thedisplay screens for presentation upon the assistant display 24. Forexample, the artificial intelligence engine 11 may generate recommendedtreatment plans and/or excluded treatment plans for patients andgenerate the display screens including those recommended treatment plansand/or external treatment plans for presentation on the assistantdisplay 24 of the assistant interface 94. In some embodiments, theassistant display 24 may be configured to present a virtualized desktophosted by the server 30. In some embodiments, the server 30 may beconfigured to communicate with the assistant interface 94 via the firstnetwork 34. In some embodiments, the first network 34 may include alocal area network (LAN), such as an Ethernet network.

In some embodiments, the first network 34 may include the Internet, andcommunications between the server 30 and the assistant interface 94 maybe secured via privacy enhancing technologies, such as, for example, byusing encryption over a virtual private network (VPN). Alternatively oradditionally, the server 30 may be configured to communicate with theassistant interface 94 via one or more networks independent of the firstnetwork 34 and/or other communication means, such as a direct wired orwireless communication channel. In some embodiments, the patientinterface 50 and the treatment device 70 may each operate from a patientlocation geographically separate from a location of the assistantinterface 94. For example, the patient interface 50 and the treatmentdevice 70 may be used as part of an in-home rehabilitation system, whichmay be aided remotely by using the assistant interface 94 at acentralized location, such as a clinic or a call center.

In some embodiments, the assistant interface 94 may be one of severaldifferent terminals (e.g., computing devices) that may be groupedtogether, for example, in one or more call centers or at one or moreclinicians' offices. In some embodiments, a plurality of assistantinterfaces 94 may be distributed geographically. In some embodiments, aperson may work as a healthcare provider remotely from any conventionaloffice infrastructure. Such remote work may be performed, for example,where the assistant interface 94 takes the form of a computer and/ortelephone. This remote work functionality may allow for work-from-homearrangements that may include part time and/or flexible work hours for ahealthcare provider.

FIGS. 2-3 show an embodiment of a treatment device 70. Morespecifically, FIG. 2 generally illustrates a treatment device 70 in theform of a stationary cycling machine 100, which may be called astationary bike, for short. The stationary cycling machine 100 includesa set of pedals 102 each attached to a pedal arm 104 for rotation aboutan axle 106. In some embodiments, and as is generally illustrated inFIG. 2, the pedals 102 are movable on the pedal arms 104 in order toadjust a range of motion used by the patient in pedaling. For example,the pedals being located inwardly toward the axle 106 corresponds to asmaller range of motion than when the pedals are located outwardly awayfrom the axle 106. One or more pressure sensors 86 is attached to orembedded within one or both of the pedals 102 for measuring an amount offorce applied by the patient on a pedal 102. The pressure sensor 86 maycommunicate wirelessly to the treatment device 70 and/or to the patientinterface 50.

FIG. 4 generally illustrates a person (a patient) using the treatmentdevice of FIG. 2 and showing sensors and various data parametersconnected to a patient interface 50. The example patient interface 50 isa tablet computer or smartphone, or a phablet, such as an iPad, aniPhone, an Android device, or a Surface tablet, which is held manuallyby the patient. In some other embodiments, the patient interface 50 maybe embedded within or attached to the treatment device 70.

FIG. 4 generally illustrates the patient wearing the ambulation sensor82 on his wrist, with a note showing “STEPS TODAY 1355”, indicating thatthe ambulation sensor 82 has recorded and transmitted that step count tothe patient interface 50. FIG. 4 also generally illustrates the patientwearing the goniometer 84 on his right knee, with a note showing “KNEEANGLE 72°”, indicating that the goniometer 84 is measuring andtransmitting that knee angle to the patient interface 50. FIG. 4 alsogenerally illustrates a right side of one of the pedals 102 with apressure sensor 86 showing “FORCE 12.5 lbs.,” indicating that the rightpedal pressure sensor 86 is measuring and transmitting that forcemeasurement to the patient interface 50.

FIG. 4 also generally illustrates a left side of one of the pedals 102with a pressure sensor 86 showing “FORCE 27 lbs.”, indicating that theleft pedal pressure sensor 86 is measuring and transmitting that forcemeasurement to the patient interface 50. FIG. 4 also generallyillustrates other patient data, such as an indicator of “SESSION TIME0:04:13”, indicating that the patient has been using the treatmentdevice 70 for 4 minutes and 13 seconds. This session time may bedetermined by the patient interface 50 based on information receivedfrom the treatment device 70. FIG. 4 also generally illustrates anindicator showing “PAIN LEVEL 3”. Such a pain level may be obtained fromthe patent in response to a solicitation, such as a question, presentedupon the patient interface 50.

FIG. 5 is an example embodiment of an overview display 120 of theassistant interface 94. Specifically, the overview display 120 presentsseveral different controls and interfaces for the healthcare provider toremotely assist a patient with using the patient interface 50 and/or thetreatment device 70. This remote assistance functionality may also becalled telemedicine or telehealth.

Specifically, the overview display 120 includes a patient profiledisplay 130 presenting biographical information regarding a patientusing the treatment device 70. The patient profile display 130 may takethe form of a portion or region of the overview display 120, as isgenerally illustrated in FIG. 5, although the patient profile display130 may take other forms, such as a separate screen or a popup window.

In some embodiments, the patient profile display 130 may include alimited subset of the patient's biographical information. Morespecifically, the data presented upon the patient profile display 130may depend upon the healthcare provider's need for that information. Forexample, a healthcare provider that is assisting the patient with amedical issue may be provided with medical history information regardingthe patient, whereas a technician troubleshooting an issue with thetreatment device 70 may be provided with a much more limited set ofinformation regarding the patient. The technician, for example, may begiven only the patient's name.

The patient profile display 130 may include pseudonymized data and/oranonymized data or use any privacy enhancing technology to preventconfidential patient data from being communicated in a way that couldviolate patient confidentiality requirements. Such privacy enhancingtechnologies may enable compliance with laws, regulations, or otherrules of governance such as, but not limited to, the Health InsurancePortability and Accountability Act (HIPAA), or the General DataProtection Regulation (GDPR), wherein the patient may be deemed a “datasubject”.

In some embodiments, the patient profile display 130 may presentinformation regarding the treatment plan for the patient to follow inusing the treatment device 70. Such treatment plan information may belimited to a healthcare provider. For example, a healthcare providerassisting the patient with an issue regarding the treatment regimen maybe provided with treatment plan information, whereas a techniciantroubleshooting an issue with the treatment device 70 may not beprovided with any information regarding the patient's treatment plan.

In some embodiments, one or more recommended treatment plans and/orexcluded treatment plans may be presented in the patient profile display130 to the healthcare provider. The one or more recommended treatmentplans and/or excluded treatment plans may be generated by the artificialintelligence engine 11 of the server 30 and received from the server 30in real-time during a telemedicine or telehealth session. An example ofpresenting the one or more recommended treatment plans and/or ruled-outtreatment plans is described below with reference to FIG. 7.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes a patient status display 134 presenting status informationregarding a patient using the treatment device. The patient statusdisplay 134 may take the form of a portion or region of the overviewdisplay 120, as is generally illustrated in FIG. 5, although the patientstatus display 134 may take other forms, such as a separate screen or apopup window.

The patient status display 134 includes sensor data 136 from one or moreof the external sensors 82, 84, 86, and/or from one or more internalsensors 76 of the treatment device 70. In some embodiments, the patientstatus display 134 may include sensor data from one or more sensors ofone or more wearable devices worn by the patient while using thetreatment device 70. The one or more wearable devices may include awatch, a bracelet, a necklace, a chest strap, and the like. The one ormore wearable devices may be configured to monitor a heartrate, atemperature, a blood pressure, one or more vital signs, and the like ofthe patient while the patient is using the treatment device 70. In someembodiments, the patient status display 134 may present other data 138regarding the patient, such as last reported pain level, or progresswithin a treatment plan.

User access controls may be used to limit access, including what data isavailable to be viewed and/or modified, on any or all of the userinterfaces 20, 50, 90, 92, 94 of the system 10. In some embodiments,user access controls may be employed to control what information isavailable to any given person using the system 10. For example, datapresented on the assistant interface 94 may be controlled by user accesscontrols, with permissions set depending on the healthcareprovider/user's need for and/or qualifications to view that information.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes a help data display 140 presenting information for thehealthcare provider to use in assisting the patient. The help datadisplay 140 may take the form of a portion or region of the overviewdisplay 120, as is generally illustrated in FIG. 5. The help datadisplay 140 may take other forms, such as a separate screen or a popupwindow. The help data display 140 may include, for example, presentinganswers to frequently asked questions regarding use of the patientinterface 50 and/or the treatment device 70.

The help data display 140 may also include research data or bestpractices. In some embodiments, the help data display 140 may presentscripts for answers or explanations in response to patient questions. Insome embodiments, the help data display 140 may present flow charts orwalk-throughs for the healthcare provider to use in determining a rootcause and/or solution to a patient's problem.

In some embodiments, the assistant interface 94 may present two or morehelp data displays 140, which may be the same or different, forsimultaneous presentation of help data for use by the healthcareprovider. for example, a first help data display may be used to presenta troubleshooting flowchart to determine the source of a patient'sproblem, and a second help data display may present script informationfor the healthcare provider to read to the patient, such information topreferably include directions for the patient to perform some action,which may help to narrow down or solve the problem. In some embodiments,based upon inputs to the troubleshooting flowchart in the first helpdata display, the second help data display may automatically populatewith script information.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes a patient interface control 150 presenting informationregarding the patient interface 50, and/or to modify one or moresettings of the patient interface 50. The patient interface control 150may take the form of a portion or region of the overview display 120, asis generally illustrated in FIG. 5. The patient interface control 150may take other forms, such as a separate screen or a popup window. Thepatient interface control 150 may present information communicated tothe assistant interface 94 via one or more of the interface monitorsignals 98 b.

As is generally illustrated in FIG. 5, the patient interface control 150includes a display feed 152 of the display presented by the patientinterface 50. In some embodiments, the display feed 152 may include alive copy of the display screen currently being presented to the patientby the patient interface 50. In other words, the display feed 152 maypresent an image of what is presented on a display screen of the patientinterface 50.

In some embodiments, the display feed 152 may include abbreviatedinformation regarding the display screen currently being presented bythe patient interface 50, such as a screen name or a screen number. Thepatient interface control 150 may include a patient interface settingcontrol 154 for the healthcare provider to adjust or to control one ormore settings or aspects of the patient interface 50. In someembodiments, the patient interface setting control 154 may cause theassistant interface 94 to generate and/or to transmit an interfacecontrol signal 98 for controlling a function or a setting of the patientinterface 50.

In some embodiments, the patient interface setting control 154 mayinclude collaborative browsing or co-browsing capability for thehealthcare provider to remotely view and/or control the patientinterface 50. For example, the patient interface setting control 154 mayenable the healthcare provider to remotely enter text to one or moretext entry fields on the patient interface 50 and/or to remotely controla cursor on the patient interface 50 using a mouse or touchscreen of theassistant interface 94.

In some embodiments, using the patient interface 50, the patientinterface setting control 154 may allow the healthcare provider tochange a setting that cannot be changed by the patient. For example, thepatient interface 50 may be precluded from accessing a language settingto prevent a patient from inadvertently switching, on the patientinterface 50, the language used for the displays, whereas the patientinterface setting control 154 may enable the healthcare provider tochange the language setting of the patient interface 50. In anotherexample, the patient interface 50 may not be able to change a font sizesetting to a smaller size in order to prevent a patient frominadvertently switching the font size used for the displays on thepatient interface 50 such that the display would become illegible to thepatient, whereas the patient interface setting control 154 may providefor the healthcare provider to change the font size setting of thepatient interface 50.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes an interface communications display 156 showing the status ofcommunications between the patient interface 50 and one or more otherdevices 70, 82, 84, such as the treatment device 70, the ambulationsensor 82, and/or the goniometer 84. The interface communicationsdisplay 156 may take the form of a portion or region of the overviewdisplay 120, as is generally illustrated in FIG. 5.

The interface communications display 156 may take other forms, such as aseparate screen or a popup window. The interface communications display156 may include controls for the healthcare provider to remotely modifycommunications with one or more of the other devices 70, 82, 84. Forexample, the healthcare provider may remotely command the patientinterface 50 to reset communications with one of the other devices 70,82, 84, or to establish communications with a new one of the otherdevices 70, 82, 84. This functionality may be used, for example, wherethe patient has a problem with one of the other devices 70, 82, 84, orwhere the patient receives a new or a replacement one of the otherdevices 70, 82, 84.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes an apparatus control 160 for the healthcare provider to viewand/or to control information regarding the treatment device 70. Theapparatus control 160 may take the form of a portion or region of theoverview display 120, as is generally illustrated in FIG. 5. Theapparatus control 160 may take other forms, such as a separate screen ora popup window. The apparatus control 160 may include an apparatusstatus display 162 with information regarding the current status of theapparatus. The apparatus status display 162 may present informationcommunicated to the assistant interface 94 via one or more of theapparatus monitor signals 99 b. The apparatus status display 162 mayindicate whether the treatment device 70 is currently communicating withthe patient interface 50. The apparatus status display 162 may presentother current and/or historical information regarding the status of thetreatment device 70.

The apparatus control 160 may include an apparatus setting control 164for the healthcare provider to modify or control one or more aspects ofthe treatment device 70. The apparatus setting control 164 may cause theassistant interface 94 to generate and/or to transmit an apparatuscontrol signal 99 (e.g., which may be referred to as treatment planinput, as described) for changing an operating parameter and/or one ormore characteristics of the treatment device 70, (e.g., a pedal radiussetting, a resistance setting, a target RPM, other suitablecharacteristics of the treatment device 70, or a combination thereof).

The apparatus setting control 164 may include a mode button 166 and aposition control 168, which may be used in conjunction for thehealthcare provider to place an actuator 78 of the treatment device 70in a manual mode, after which a setting, such as a position or a speedof the actuator 78, can be changed using the position control 168. Themode button 166 may provide for a setting, such as a position, to betoggled between automatic and manual modes.

In some embodiments, one or more settings may be adjustable at any time,and without having an associated auto/manual mode. In some embodiments,the healthcare provider may change an operating parameter of thetreatment device 70, such as a pedal radius setting, while the patientis actively using the treatment device 70. Such “on the fly” adjustmentmay or may not be available to the patient using the patient interface50.

In some embodiments, the apparatus setting control 164 may allow thehealthcare provider to change a setting that cannot be changed by thepatient using the patient interface 50. For example, the patientinterface 50 may be precluded from changing a preconfigured setting,such as a height or a tilt setting of the treatment device 70, whereasthe apparatus setting control 164 may provide for the healthcareprovider to change the height or tilt setting of the treatment device70.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes a patient communications control 170 for controlling an audioor an audiovisual communications session with the patient interface 50.The communications session with the patient interface 50 may comprise alive feed from the assistant interface 94 for presentation by the outputdevice of the patient interface 50. The live feed may take the form ofan audio feed and/or a video feed. In some embodiments, the patientinterface 50 may be configured to provide two-way audio or audiovisualcommunications with a person using the assistant interface 94.Specifically, the communications session with the patient interface 50may include bidirectional (two-way) video or audiovisual feeds, witheach of the patient interface 50 and the assistant interface 94presenting video of the other one.

In some embodiments, the patient interface 50 may present video from theassistant interface 94, while the assistant interface 94 presents onlyaudio or the assistant interface 94 presents no live audio or visualsignal from the patient interface 50. In some embodiments, the assistantinterface 94 may present video from the patient interface 50, while thepatient interface 50 presents only audio or the patient interface 50presents no live audio or visual signal from the assistant interface 94.

In some embodiments, the audio or an audiovisual communications sessionwith the patient interface 50 may take place, at least in part, whilethe patient is performing the rehabilitation regimen upon the body part.The patient communications control 170 may take the form of a portion orregion of the overview display 120, as is generally illustrated in FIG.5. The patient communications control 170 may take other forms, such asa separate screen or a popup window.

The audio and/or audiovisual communications may be processed and/ordirected by the assistant interface 94 and/or by another device ordevices, such as a telephone system, or a videoconferencing system usedby the healthcare provider while the healthcare provider uses theassistant interface 94. Alternatively or additionally, the audio and/oraudiovisual communications may include communications with a thirdparty. For example, the system 10 may enable the healthcare provider toinitiate a 3-way conversation regarding use of a particular piece ofhardware or software, with the patient and a subject matter expert, suchas a healthcare provider or a specialist. The example patientcommunications control 170 generally illustrated in FIG. 5 includes callcontrols 172 for the healthcare provider to use in managing variousaspects of the audio or audiovisual communications with the patient. Thecall controls 172 include a disconnect button 174 for the healthcareprovider to end the audio or audiovisual communications session. Thecall controls 172 also include a mute button 176 to temporarily silencean audio or audiovisual signal from the assistant interface 94. In someembodiments, the call controls 172 may include other features, such as ahold button (not shown).

The call controls 172 also include one or more record/playback controls178, such as record, play, and pause buttons to control, with thepatient interface 50, recording and/or playback of audio and/or videofrom the teleconference session. The call controls 172 also include avideo feed display 180 for presenting still and/or video images from thepatient interface 50, and a self-video display 182 showing the currentimage of the healthcare provider using the assistant interface 94. Theself-video display 182 may be presented as a picture-in-picture format,within a section of the video feed display 180, as is generallyillustrated in FIG. 5. Alternatively or additionally, the self-videodisplay 182 may be presented separately and/or independently from thevideo feed display 180.

The example overview display 120 generally illustrated in FIG. 5 alsoincludes a third party communications control 190 for use in conductingaudio and/or audiovisual communications with a third party. The thirdparty communications control 190 may take the form of a portion orregion of the overview display 120, as is generally illustrated in FIG.5. The third party communications control 190 may take other forms, suchas a display on a separate screen or a popup window.

The third party communications control 190 may include one or morecontrols, such as a contact list and/or buttons or controls to contact athird party regarding use of a particular piece of hardware or software,e.g., a subject matter expert, such as a healthcare provider or aspecialist. The third party communications control 190 may includeconference calling capability for the third party to simultaneouslycommunicate with both the healthcare provider via the assistantinterface 94, and with the patient via the patient interface 50. Forexample, the system 10 may provide for the healthcare provider toinitiate a 3-way conversation with the patient and the third party.

FIG. 6 generally illustrates an example block diagram of training amachine learning model 13 to output, based on data 600 pertaining to thepatient, a treatment plan 602 for the patient according to the presentdisclosure. Data pertaining to other patients may be received by theserver 30. The other patients may have used various treatment devices toperform treatment plans.

The data may include characteristics of the other patients, the detailsof the treatment plans performed by the other patients, and/or theresults of performing the treatment plans (e.g., a percent of recoveryof a portion of the patients' bodies, an amount of recovery of a portionof the patients' bodies, an amount of increase or decrease in musclestrength of a portion of patients' bodies, an amount of increase ordecrease in range of motion of a portion of patients' bodies, etc.).

As depicted, the data has been assigned to different cohorts. Cohort Aincludes data for patients having similar first characteristics, firsttreatment plans, and first results. Cohort B includes data for patientshaving similar second characteristics, second treatment plans, andsecond results. For example, cohort A may include first characteristicsof patients in their twenties without any medical conditions whounderwent surgery for a broken limb; their treatment plans may include acertain treatment protocol (e.g., use the treatment device 70 for 30minutes 5 times a week for 3 weeks, wherein values for the properties,configurations, and/or settings of the treatment device 70 are set to X(where X is a numerical value) for the first two weeks and to Y (where Yis a numerical value) for the last week).

Cohort A and cohort B may be included in a training dataset used totrain the machine learning model 13. The machine learning model 13 maybe trained to match a pattern between characteristics for each cohortand output the treatment plan or a variety of possible treatment plansfor selection by a healthcare provider that provides the result.Accordingly, when the data 600 for a new patient is input into thetrained machine learning model 13, the trained machine learning model 13may match the characteristics included in the data 600 withcharacteristics in either cohort A or cohort B and output theappropriate treatment plan or plans 602. In some embodiments, themachine learning model 13 may be trained to output one or more excludedtreatment plans that should not be performed by the new patient.

FIG. 7 generally illustrates an embodiment of an overview display 120 ofthe assistant interface 94 presenting recommended treatment plans andexcluded treatment plans in real-time during a telemedicine sessionaccording to the present disclosure. As depicted, the overview display120 just includes sections for the patient profile 130 and the videofeed display 180, including the self-video display 182. Any suitableconfiguration of controls and interfaces of the overview display 120described with reference to FIG. 5 may be presented in addition to orinstead of the patient profile 130, the video feed display 180, and theself-video display 182.

The healthcare provider using the assistant interface 94 (e.g.,computing device) during the telemedicine session may be presented inthe self-video 182 in a portion of the overview display 120 (e.g., userinterface presented on a display screen 24 of the assistant interface94) that also presents a video from the patient in the video feeddisplay 180. Further, the video feed display 180 may also include agraphical user interface (GUI) object 700 (e.g., a button) that enablesthe healthcare provider to share, in real-time or near real-time duringthe telemedicine session, the recommended treatment plans and/or theexcluded treatment plans with the patient on the patient interface 50.The healthcare provider may select the GUI object 700 to share therecommended treatment plans and/or the excluded treatment plans. Asdepicted, another portion of the overview display 120 includes thepatient profile display 130.

The patient profile display 130 is presenting two example recommendedtreatment plans 600 and one example excluded treatment plan 602. Asdescribed herein, the treatment plans may be recommended in view ofcharacteristics of the patient being treated. To generate therecommended treatment plans 600 the patient should follow to achieve adesired result, a pattern between the characteristics of the patientbeing treated and a cohort of other people who have used the treatmentdevice 70 to perform a treatment plan may be matched by one or moremachine learning models 13 of the artificial intelligence engine 11.Each of the recommended treatment plans may be generated based ondifferent desired results.

For example, as depicted, the patient profile display 130 presents “Thecharacteristics of the patient match characteristics of uses in CohortA. The following treatment plans are recommended for the patient basedon his characteristics and desired results.” Then, the patient profiledisplay 130 presents recommended treatment plans from cohort A, and eachtreatment plan provides different results.

As depicted, treatment plan “A” indicates “Patient X should usetreatment device for 30 minutes a day for 4 days to achieve an increasedrange of motion of Y %; Patient X has Type 2 Diabetes; and Patient Xshould be prescribed medication Z for pain management during thetreatment plan (medication Z is approved for people having Type 2Diabetes).” Accordingly, the treatment plan generated achievesincreasing the range of motion of Y %. As may be appreciated, thetreatment plan also includes a recommended medication (e.g., medicationZ) to prescribe to the patient to manage pain in view of a known medicaldisease (e.g., Type 2 Diabetes) of the patient. That is, the recommendedpatient medication not only does not conflict with the medical conditionof the patient but thereby improves the probability of a superiorpatient outcome. This specific example and all such examples elsewhereherein are not intended to limit in any way the generated treatment planfrom recommending multiple medications, or from handling theacknowledgement, view, diagnosis and/or treatment of comorbid conditionsor diseases.

Recommended treatment plan “B” may specify, based on a different desiredresult of the treatment plan, a different treatment plan including adifferent treatment protocol for a treatment device, a differentmedication regimen, etc.

As depicted, the patient profile display 130 may also present theexcluded treatment plans 602. These types of treatment plans are shownto the healthcare provider using the assistant interface 94 to alert thehealthcare provider not to recommend certain portions of a treatmentplan to the patient. For example, the excluded treatment plan couldspecify the following: “Patient X should not use treatment device forlonger than 30 minutes a day due to a heart condition; Patient X hasType 2 Diabetes; and Patient X should not be prescribed medication M forpain management during the treatment plan (in this scenario, medicationM can cause complications for people having Type 2 Diabetes).Specifically, the excluded treatment plan points out a limitation of atreatment protocol where, due to a heart condition, Patient X should notexercise for more than 30 minutes a day. The ruled-out treatment planalso points out that Patient X should not be prescribed medication Mbecause it conflicts with the medical condition Type 2 Diabetes.

The healthcare provider may select the treatment plan for the patient onthe overview display 120. For example, the healthcare provider may usean input peripheral (e.g., mouse, touchscreen, microphone, keyboard,etc.) to select from the treatment plans 600 for the patient. In someembodiments, during the telemedicine session, the healthcare providermay discuss the pros and cons of the recommended treatment plans 600with the patient.

In any event, the healthcare provider may select the treatment plan forthe patient to follow to achieve the desired result. The selectedtreatment plan may be transmitted to the patient interface 50 forpresentation. The patient may view the selected treatment plan on thepatient interface 50. In some embodiments, the healthcare provider andthe patient may discuss during the telemedicine session the details(e.g., treatment protocol using treatment device 70, diet regimen,medication regimen, etc.) in real-time or in near real-time. In someembodiments, the server 30 may control, based on the selected treatmentplan and during the telemedicine session, the treatment device 70 as theuser uses the treatment device 70.

FIG. 8 generally illustrates an embodiment of the overview display 120of the assistant interface 94 presenting, in real-time during atelemedicine session, recommended treatment plans that have changed as aresult of patient data changing according to the present disclosure. Asmay be appreciated, the treatment device 70 and/or any computing device(e.g., patient interface 50) may transmit data while the patient usesthe treatment device 70 to perform a treatment plan. The data mayinclude updated characteristics of the patient and/or other treatmentdata. For example, the updated characteristics may include newperformance information and/or measurement information. The performanceinformation may include a speed of a portion of the treatment device 70,a range of motion achieved by the patient, a force exerted on a portionof the treatment device 70, a heartrate of the patient, a blood pressureof the patient, a respiratory rate of the patient, and so forth.

In some embodiments, the data received at the server 30 may be inputinto the trained machine learning model 13, which may determine that thecharacteristics indicate the patient is on track for the currenttreatment plan. Determining the patient is on track for the currenttreatment plan may cause the trained machine learning model 13 to modifya parameter of the treatment device 70. The modification may be based ona next step of the treatment plan to further improve the performance ofthe patient.

In some embodiments, the data received at the server 30 may be inputinto the trained machine learning model 13, which may determine that thecharacteristics indicate the patient is not on track (e.g., behindschedule, not able to maintain a speed, not able to achieve a certainrange of motion, is in too much pain, etc.) for the current treatmentplan or is ahead of schedule (e.g., exceeding a certain speed,exercising longer than specified with no pain, exerting more than aspecified force, etc.) for the current treatment plan.

The trained machine learning model 13 may determine that thecharacteristics of the patient no longer match the characteristics ofthe patients in the cohort to which the patient is assigned.Accordingly, the trained machine learning model 13 may reassign thepatient to another cohort that includes qualifying characteristics thepatient's characteristics. As such, the trained machine learning model13 may select a new treatment plan from the new cohort and control,based on the new treatment plan, the treatment device 70.

In some embodiments, prior to controlling the treatment device 70, theserver 30 may provide the new treatment plan 800 to the assistantinterface 94 for presentation in the patient profile 130. As depicted,the patient profile 130 indicates “The characteristics of the patienthave changed and now match characteristics of uses in Cohort B. Thefollowing treatment plan is recommended for the patient based on hischaracteristics and desired results.” Then, the patient profile 130presents the new treatment plan 800 (“Patient X should use the treatmentdevice for 10 minutes a day for 3 days to achieve an increased range ofmotion of L %.” The healthcare provider may select the new treatmentplan 800, and the server 30 may receive the selection. The server 30 maycontrol the treatment device 70 based on the new treatment plan 800. Insome embodiments, the new treatment plan 800 may be transmitted to thepatient interface 50 such that the patient may view the details of thenew treatment plan 800.

In some embodiments, the server 30 may be configured to receivetreatment data pertaining to a user who uses the treatment device 70 toperform the treatment plan. The user may include a patient, user, orperson using the treatment device to perform various exercises. Thetreatment data may include various characteristics of the user, variousmeasurement information pertaining to the user while the user uses thetreatment device 70, various characteristics of the treatment device 70,the treatment plan, other suitable data, or a combination thereof. Insome embodiments, the sever 30 may be configured to receive thetreatment data during a telemedicine session.

In some embodiments, while the user uses the treatment device 70 toperform the treatment plan, at least some of the treatment data mayinclude the sensor data 136 from one or more of the external sensors 82,84, 86, and/or from one or more internal sensors 76 of the treatmentdevice 70. Any sensor referred to herein may be standalone, part of aneural net, a node on the Internet of Things, or otherwise connected orconfigured to be connected to a physical or wireless network. Further,any sensor referred to herein may itself be electronic in itsembodiment, e.g., a biometric retinal scanner, which senses, viaelectromagnetic or other means, a retina and generates a retinalbiometric fingerprint. In some embodiments, at least some of thetreatment data may include sensor data from one or more sensors of oneor more wearable devices worn by the user while using the treatmentdevice 70. The one or more wearable devices may include a watch, abracelet, a necklace, a headband, a wristband, an ankle band, eyeglassesor eyewear (such as, without limitation, Google Glass) a chest or torsostrap, a device configured to be work on, attached to, orcommunicatively coupled to a body, and the like. While the user is usingthe treatment device 70, the one or more wearable devices may beconfigured to monitor, with respect to the user, a heartrate, atemperature, a blood pressure, eye dilation, one or more vital signs,one or more metabolic markers, biomarkers, microbiome-related data, andthe like.

The various characteristics of the treatment device 70 may include oneor more settings of the treatment device 70, a current revolutions pertime period (e.g., such as one minute) of a rotating member (e.g., suchas a wheel) of the treatment device 70, a resistance setting of thetreatment device 70, other suitable characteristics of the treatmentdevice 70, or a combination thereof. The measurement information mayinclude one or more vital signs of the user, a respiration rate of theuser, a heartrate of the user, a temperature of the user, a bloodpressure of the user, a blood oxygen level (e.g., SpO2) of the user, aglucose level of the user, other suitable measurement information of theuser, microbiome related data pertaining to the user, or a combinationthereof, a blood oxygen level (e.g., SpO2) of the user,

Additionally, or alternatively, the treatment data may include variousaspects of the treatment plan, including one or more treatmentschedules, one or more appointments, other suitable aspects of thetreatment plan, or a combination thereof. A treatment schedule maydefine a cadence, interval, or frequency for the user to perform variousaspects of the treatment plan. For example, a treatment scheduleassociated with the treatment plan may define, for a period, a number oftreatment sessions during which the user is indicated to perform one ormore exercises defined by the treatment plan.

In some embodiments, the server 30 may be configured to write thetreatment data to an associated memory, such as the memory 62, othersuitable memory, or a combination thereof. The associated memory may beconfigured to be accessed by an artificial intelligence engine, such asthe artificial intelligence engine 11. The artificial intelligenceengine 11 may configured to use at least one machine learning model,such as the machine learning model 13, to generate, using the treatmentdata, at least one of a treatment scheduling output prediction and anappointment output.

In some embodiments, the treatment scheduling output predictiongenerated by the machine learning model 13 may indicate, at least, acadence, interval, or frequency for the user to perform various aspectsof the treatment plan. For example, the machine learning model 13 maydetermine, based on the treatment data, that a predicted cadence,interval, or frequency for the user to perform the various aspects ofthe treatment plan may yield one or more expected results, such as anexpected performance of the user, an expected progress of the user, andthe like. The treatment scheduling output prediction may include aprobabilistic prediction, stochastic prediction, or a deterministicprediction.

In some embodiments, the appointment output may indicate a period for afuture appointment or two or periods corresponding to respective futureappointments. For example, the machine learning model 13 may determine,based on the treatment data, that the user may benefit from a singlefuture appointment set during a first period. The first period mayinclude a timeframe (e.g., in days, weeks, months, and so on) forsetting the single future appointment.

Additionally, or alternatively, the machine learning model 13 maydetermine, based on the treatment data, that the user may benefit fromtwo or more future appointments. The machine learning model 13 mayindicate, in the appointment output, a first period for setting a firstappointment of the two or more appointments, a second period for settinga second of the two or more appointments, and so on. Each periodindicated by the machine learning model 13 may include a correspondingtimeframe (e.g., in days, weeks, months, and so on). The timeframe foreach respective period may be the same or different.

In some embodiments, the server may be configured to receive, from theartificial intelligence engine 11, the at least one of the treatmentscheduling output prediction and the appointment output. The server 30may selectively modify, using the at least one of the treatmentscheduling output prediction and the appointment output, one or moreaspects of the treatment plan. For example, the server 30, based on thetreatment scheduling output prediction, may modify the treatmentschedule of the treatment plan. Additionally, or alternatively, theserver 30, based on the appointment output, may modify one or moreappointments of the treatment plan.

In some embodiments, the server 30 may receive other treatment datapertaining to at least one other user who uses at least one of thetreatment device 70 or another treatment device to perform anothertreatment plan. The at least one other user may include one or moreother patients, users, or persons using the treatment device or othercorresponding treatment devices to perform various exercises. The othertreatment data may include various characteristics of the at least oneother user, various measurement information pertaining to the at leastone other user while the at least one other user uses the treatmentdevice 70 or another treatment device, various characteristics of thetreatment device 70 or the another treatment device, the other treatmentplan, other suitable data, or a combination thereof. In someembodiments, the server 30 may receive the other treatment data during acorresponding telemedicine session.

In some embodiments, the server 30 may determine, using the artificialintelligence engine 11 that uses the machine learning model 13, whetherto group or a probability or other statistical measure of grouping theuser with the at least one other user. The server 30 may determine,using the artificial intelligence engine 11 using the machine learningmodel 13, that the treatment data and the other treatment data share atleast some similar aspects. In response to a determination that thetreatment data and the other treatment data share at least some similaraspects, the server 30 may determine that grouping the user and the atleast one other user may be beneficial to at least one of the user andthe other user. For example, the user and/or the at least one other usermay benefit from watching the other of the user and the at least oneother user perform various aspects of the treatment plan, the userand/or the at least one other user may be encouraged by the performanceof the other of the user and the at least one other user to continueperforming aspects of the treatment plan, and the like.

In response to the server 30 determining to group the user with the atleast one other user, the server 30 may be configured to group the userwith the at least one other user by at least associating (e.g.,coordinating, synchronizing, and the like) the at least one of thetreatment schedule and the at least one appointment of the treatmentplan (e.g., pertaining to the user) with the at least one of thetreatment schedule and the at least one appointment of the othertreatment plan (e.g., pertaining to the at least one other user).

In some embodiments, associating the at least one of the treatmentschedule and the at least one appointment of the treatment plan (e.g.,pertaining to the user) with the at least one of the treatment scheduleand the at least one appointment of the other treatment plan (e.g.,pertaining to the at least one other user) may include the server 30making the treatment schedule of the treatment plan identical orsubstantially similar to the treatment schedule of the other treatmentplan, setting the treatment schedule of the treatment plan on to followa cadence, interval, or frequency that alternates or otherwisecorresponds to a cadence, interval, or frequency of the treatmentschedule of the other treatment plan, setting a first appointment or oneor more appointments of the at least one appointment of the treatmentplan at the same time as a first appointment or two or more appointmentsof the at least one appointment of the other treatment plan, setting afirst appointment or two or more appointments of the at least oneappointment of the other treatment plan according to a first appointmentor two or more appointments of the treatment plan (e.g. at differenttimes but according to the same or similar period or discrete timeinterval, such as later in time but at the same number of weeks betweenappointments, for example), making, generating, or identifying othersuitable associations of the at least one of the treatment schedule andthe at least one appointment of the treatment plan (e.g., pertaining tothe user) with the at least one of the treatment schedule and the atleast one appointment of the other treatment plan (e.g., pertaining tothe at least one other user), or a combination thereof.

In some embodiments, the server 30 may be configured to group the userand/or the other user with any suitable number of other users.Additionally, or alternatively, the treatment schedule of the treatmentplan and/or the at least one appointment of the treatment plan mayprecede other treatment schedules and/or at least one other appointmentof other respective treatment plans. The systems and methods describedherein may be configured to use the treatment schedule and/or the atleast one appointment of the treatment plan as a model for othertreatment schedules and/or at least one other appointment of otherrespective treatment plans (e.g., the other treatment schedules and/orat least one other appointment of other respective treatment plans mayfollow the same or similar schedule as the treatment schedule of thetreatment plan and/or the same or similar periods for the at least oneother appointment of other respective treatment plans).

In some embodiments, the treatment plan, including the configurations,settings, range of motion settings, pain level, force settings, andspeed settings, etc. of the treatment device 70 for various exercises,may be transmitted to the controller of the treatment device 70. In oneexample, if the user provides an indication, via the patient interface50, that he is experiencing a high level of pain at a particular rangeof motion, the controller may receive the indication. Based on theindication, the controller may electronically adjust the range of motionof the pedal 102 by adjusting the pedal inwardly, outwardly, or along orabout any suitable axis, via one or more actuators, hydraulics, springs,electric motors, or the like. The treatment plan may define alternativerange of motion settings for the pedal 102 when the user indicatescertain pain levels during an exercise. Accordingly, once the treatmentplan is uploaded to the controller of the treatment device 70, thetreatment device 70 may continue to operate without further instruction,further external input, and the like. It should be noted that thepatient (via the patient interface 50) and/or the assistant (via theassistant interface 94) may override any of the configurations orsettings of the treatment device 70 at any time. For example, thepatient may use the patient interface 50 to cause the treatment device70 to immediately stop, if so desired.

FIG. 9 is a flow diagram generally illustrating a method 900 forproviding, based on treatment data pertaining to a user who uses thetreatment device 70, a recommendation to a healthcare provider,according to the principles of the present disclosure. The method 900 isperformed by processing logic that may include hardware (circuitry,dedicated logic, etc.), software (such as is run on a general-purposecomputer system or a dedicated machine), or a combination of both. Themethod 900 and/or each of its individual functions, routines,subroutines, or operations may be performed by one or more processors ofa computing device (e.g., any component of FIG. 1, such as server 30executing the artificial intelligence engine 11). In some embodiments,the method 900 may be performed by a single processing thread.Alternatively, the method 900 may be performed by two or more processingthreads, each thread implementing one or more individual functions,routines, subroutines, or operations of the methods.

For simplicity of explanation, the method 900 is depicted and describedas a series of operations. However, operations in accordance with thisdisclosure can occur in various orders and/or concurrently, and/or withother operations not presented and described herein. For example, theoperations depicted in the method 900 may occur in combination with anyother operation of any other method disclosed herein. Furthermore, notall illustrated operations may be required to implement the method 900in accordance with the disclosed subject matter. In addition, thoseskilled in the art will understand and appreciate that the method 900could alternatively be represented as a series of interrelated statesvia a state diagram or events.

At 902, the processing device may receive treatment data pertaining to auser who uses a treatment device, such as the treatment device 70, toperform a treatment plan. The treatment data may include at least one ofcharacteristics of the user, measurement information pertaining to theuser, characteristics of the treatment device, and at least one aspectof the treatment plan. The at least one aspect of the treatment plan mayinclude at least one of a treatment schedule and at least oneappointment.

At 904, the processing device may write the treatment data to anassociated memory, such as the memory 62 or other suitable memory. Theassociated memory may be configured to be accessed by an artificialintelligence engine, such as the artificial intelligence engine 11. Theartificial intelligence engine 11 may be configured to use at least onemachine learning model, such as the machine learning model 13, and togenerate, using the treatment data, at least one of a treatmentscheduling output prediction and an appointment output.

At 906, the processing device receives, from the artificial intelligenceengine 11, the at least one of the treatment scheduling outputprediction and the appointment output.

At 9080, the processing device may selectively modify, using the atleast one of the treatment scheduling output prediction and theappointment output, at least one aspect of the treatment plan.

FIG. 10 is a flow diagram generally illustrating an alternative method1000 for providing, based on treatment data pertaining to a user whouses the treatment device 70, a recommendation to a healthcare provideraccording to the principles of the present disclosure. Method 1000includes operations performed by processors of a computing device (e.g.,any component of FIG. 1, such as server 30 executing the artificialintelligence engine 11). In some embodiments, one or more operations ofthe method 1000 are implemented in computer instructions stored on amemory device and executed by a processing device. The method 1000 maybe performed in the same or a similar manner as described above inregard to method 900. The operations of the method 1000 may be performedin some combination with any of the operations of any of the methodsdescribed herein.

At 1002, the processing device may receive, from an artificialintelligence engine, such as the artificial intelligence engine 11, atleast one of the treatment scheduling output prediction and theappointment output. For example, the processing device may receive theat least one of the treatment scheduling output prediction and theappointment output generated by the machine learning model 13.

At 1004, the processing device may selectively modify, using the atleast one of the treatment scheduling output prediction and theappointment output, at least one aspect of a treatment plan. Forexample, the processing device may selectively modify, using the atleast one of the treatment scheduling output prediction and theappointment output, the at least one aspect of the treatment planassociated with the user.

At 1006, the processing device may receive other treatment datapertaining to at least one other user who uses at least one of thetreatment device 70 or another treatment device to perform anothertreatment plan. The other treatment data may include at least one ofcharacteristics of the at least one other user, measurement informationpertaining to the at least one other user, characteristics of the atleast one of the treatment device 70 and the other treatment device, andat least one aspect of the other treatment plan. The at least one aspectof the other treatment plan may correspond to the at least one aspect ofthe treatment plan. The at least one aspect of the other treatment planmay include at least one of a treatment schedule and at least oneappointment.

At 1008, the processing device may determine, using the artificialintelligence engine 11 that uses the at least one machine learningmodel, such as the machine learning model 13, whether to group or aprobability or other statistical measure of grouping the user with theat least one other user.

FIG. 11 is a flow diagram generally illustrating an alternative method1100 for providing, based on treatment data pertaining to a user whouses the treatment device 70, a recommendation to a healthcare provideraccording to the principles of the present disclosure. Method 1100includes operations performed by processors of a computing device (e.g.,any component of FIG. 1, such as server 30 executing the artificialintelligence engine 11). In some embodiments, one or more operations ofthe method 1100 are implemented in computer instructions stored on amemory device and executed by a processing device. The method 1100 maybe performed in the same or a similar manner as described above inregard to method 900 and/or method 1000. The operations of the method1100 may be performed in some combination with any of the operations ofany of the methods described herein.

At 1102, the processing device may receive, from an artificialintelligence engine, such as the artificial intelligence engine 11, atleast one of the treatment scheduling output prediction and theappointment output. For example, the processing device may receive theat least one of the treatment scheduling output prediction and theappointment output generated by the machine learning model 13.

At 1104, the processing device may selectively modify, using the atleast one of the treatment scheduling output prediction and theappointment output, at least one aspect of a treatment plan. Forexample, the processing device may selectively modify, using the atleast one of the treatment scheduling output prediction and theappointment output, the at least one aspect of the treatment planassociated with the user.

At 1106, the processing device may receive other treatment datapertaining to at least one other user who uses at least one of thetreatment device 70 or another treatment device to perform anothertreatment plan. The other treatment data may include at least one ofcharacteristics of the at least one other user, measurement informationpertaining to the at least one other user, characteristics of the atleast one of the treatment device 70 and the other treatment device, andat least one aspect of the other treatment plan. The at least one aspectof the other treatment plan may correspond to the at least one aspect ofthe treatment plan. The at least one aspect of the other treatment planmay include at least one of a treatment schedule and at least oneappointment.

At 1108, the processing device may determine, using the artificialintelligence engine 11 that uses the at least one machine learningmodel, such as the machine learning model 13, whether to group or to usea probability or other statistical measure of grouping the user with theat least one other user.

At 1110, the processing device, in response to a determination to groupthe user with the at least one other user, may group the user with theat least one other user by at least associating the at least one of thetreatment schedule and the at least one appointment of the treatmentplan with the at least one of the other user's treatment schedule andthe at least one appointment of the other user's treatment plan.

FIG. 12 generally illustrates an example embodiment of a method 1200 forreceiving a selection of an optimal treatment plan and controlling atreatment device while the patient uses the treatment device accordingto the present disclosure, based on the optimal treatment plan. Method1200 includes operations performed by processors of a computing device(e.g., any component of FIG. 1, such as server 30 executing theartificial intelligence engine 11). In some embodiments, one or moreoperations of the method 1200 are implemented in computer instructionsstored on a memory device and executed by a processing device. Themethod 1200 may be performed in the same or a similar manner asdescribed above in regard to method 900. The operations of the method1200 may be performed in some combination with any of the operations ofany of the methods described herein.

Prior to the method 1200 being executed, various optimal treatment plansmay be generated by one or more trained machine learning models 13 ofthe artificial intelligence engine 11. For example, based on a set oftreatment plans pertaining to a medical condition of a patient, the oneor more trained machine learning models 13 may generate the optimaltreatment plans. The various treatment plans may be transmitted to oneor more computing devices of a patient and/or medical professional.

At 1202 of the method 1200, the processing device may receive aselection of an optimal treatment plan from the optimal treatment plans.The selection may have been entered on a user interface presenting theoptimal treatment plans on the patient interface 50 and/or the assistantinterface 94.

At 1204, the processing device may control, while the patient uses thetreatment device 70, based on the selected optimal treatment plan, thetreatment device 70. In some embodiments, the controlling is performeddistally by the server 30. For example, if the selection is made usingthe patient interface 50, one or more control signals may be transmittedfrom the patient interface 50 to the treatment device 70 to configure,according to the selected treatment plan, a setting of the treatmentdevice 70 to control operation of the treatment device 70. Further, ifthe selection is made using the assistant interface 94, one or morecontrol signals may be transmitted from the assistant interface 94 tothe treatment device 70 to configure, according to the selectedtreatment plan, a setting of the treatment device 70 to controloperation of the treatment device 70.

It should be noted that, as the patient uses the treatment device 70,the sensors 76 may transmit measurement data to a processing device. Theprocessing device may dynamically control, according to the treatmentplan, the treatment device 70 by modifying, based on the sensormeasurements, a setting of the treatment device 70. For example, if theforce measured by the sensor 76 indicates the user is not applyingenough force to a pedal 102, the treatment plan may indicate to reducethe required amount of force for an exercise.

It should be noted that, as the patient uses the treatment device 70,the user may use the patient interface 50 to enter input pertaining to apain level experienced by the patient as the patient performs thetreatment plan. For example, the user may enter a high degree of painwhile pedaling with the pedals 102 set to a certain range of motion onthe treatment device 70. The pain level may cause the range of motion tobe dynamically adjusted based on the treatment plan. For example, thetreatment plan may specify alternative range of motion settings if acertain pain level is indicated when the user is performing an exerciseat a certain range of motion.

FIG. 13 generally illustrates an example computer system 1300 which canperform any one or more of the methods described herein, in accordancewith one or more aspects of the present disclosure. In one example,computer system 1300 may include a computing device and correspond tothe assistance interface 94, reporting interface 92, supervisoryinterface 90, clinician interface 20, server 30 (including the AI engine11), patient interface 50, ambulatory sensor 82, goniometer 84,treatment device 70, pressure sensor 86, or any suitable component ofFIG. 1. The computer system 1300 may be capable of executinginstructions implementing the one or more machine learning models 13 ofthe artificial intelligence engine 11 of FIG. 1. The computer system maybe connected (e.g., networked) to other computer systems in a LAN, anintranet, an extranet, or the Internet, including via the cloud or apeer-to-peer network.

The computer system may operate in the capacity of a server in aclient-server network environment. The computer system may be a personalcomputer (PC), a tablet computer, a wearable (e.g., wristband), aset-top box (STB), a personal Digital Assistant (PDA), a mobile phone, acamera, a video camera, an Internet of Things (IoT) device, or anydevice capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that device. Further,while only a single computer system is illustrated, the term “computer”shall also be taken to include any collection of computers thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methods discussed herein.

The computer system 1300 includes a processing device 1302, a mainmemory 1304 (e.g., read-only memory (ROM), flash memory, solid statedrives (SSDs), dynamic random access memory (DRAM) such as synchronousDRAM (SDRAM)), a static memory 1306 (e.g., flash memory, solid statedrives (SSDs), static random access memory (SRAM)), and a data storagedevice 1308, which communicate with each other via a bus 1310.

Processing device 1302 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 1302 may be a complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or a processor implementing other instruction sets orprocessors implementing a combination of instruction sets. Theprocessing device 1402 may also be one or more special-purposeprocessing devices such as an application specific integrated circuit(ASIC), a system on a chip, a field programmable gate array (FPGA), adigital signal processor (DSP), network processor, or the like. Theprocessing device 1402 is configured to execute instructions forperforming any of the operations and steps discussed herein.

The computer system 1300 may further include a network interface device1312. The computer system 1300 also may include a video display 1314(e.g., a liquid crystal display (LCD), a light-emitting diode (LED), anorganic light-emitting diode (OLED), a quantum LED, a cathode ray tube(CRT), a shadow mask CRT, an aperture grille CRT, a monochrome CRT), oneor more input devices 1316 (e.g., a keyboard and/or a mouse or agaming-like control), and one or more speakers 1318 (e.g., a speaker).In one illustrative example, the video display 1314 and the inputdevice(s) 1316 may be combined into a single component or device (e.g.,an LCD touch screen).

The data storage device 1316 may include a computer-readable medium 1320on which the instructions 1322 embodying any one or more of the methods,operations, or functions described herein is stored. The instructions1322 may also reside, completely or at least partially, within the mainmemory 1304 and/or within the processing device 1302 during executionthereof by the computer system 1300. As such, the main memory 1304 andthe processing device 1302 also constitute computer-readable media. Theinstructions 1322 may further be transmitted or received over a networkvia the network interface device 1312.

While the computer-readable storage medium 1320 is generally illustratedin the illustrative examples to be a single medium, the term“computer-readable storage medium” should be taken to include a singlemedium or multiple media (e.g., a centralized or distributed database,and/or associated caches and servers) that store the one or more sets ofinstructions. The term “computer-readable storage medium” shall also betaken to include any medium that is capable of storing, encoding orcarrying a set of instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies of thepresent disclosure. The term “computer-readable storage medium” shallaccordingly be taken to include, but not be limited to, solid-statememories, optical media, and magnetic media.

Clause 1. A computer-implemented system, comprising: a treatment deviceconfigured to be manipulated by a user while the user performs atreatment plan; a patient interface comprising an output deviceconfigured to present telemedicine information associated with atelemedicine session; and a computing device configured to: receivetreatment data pertaining to a user who uses the treatment device toperform the treatment plan, wherein the treatment data comprises atleast one of characteristics of the user, measurement informationpertaining to the user, characteristics of the treatment device, and atleast one aspect of the treatment plan, wherein the at least one aspectof the treatment plan includes at least one of a treatment schedule andat least one appointment; write to an associated memory, configured tobe accessed by an artificial intelligence engine, the treatment data,the artificial intelligence engine being configured to use at least onemachine learning model to, using the treatment data, generate at leastone of a treatment scheduling output prediction and an appointmentoutput; receive, from the artificial intelligence engine, the at leastone of the treatment scheduling output prediction and the appointmentoutput; and selectively modify, using the at least one of the treatmentscheduling output prediction and the appointment output, the at leastone aspect of the treatment plan.

Clause 2. The computer-implemented system of any clause herein, whereinat least some of the treatment data corresponds to at least some sensordata from a sensor associated with the treatment device.

Clause 3. The computer-implemented system of any clause herein, whereinat least some of the treatment data corresponds to at least some sensordata from a sensor associated with a wearable device worn by the userwhile the user uses the treatment device.

Clause 4. The computer-implemented system of any clause herein, whereinmeasurement information includes, while the user uses the treatmentdevice, at least one of a vital sign of the user, a respiration rate ofthe user, a heartrate of the user, a temperature of the user, a bloodpressure of the user, a blood oxygen level (e.g., SpO2) of the user, andmicrobiome information of the user.

Clause 5. The computer-implemented system of any clause herein, whereinthe at least one machine learning model includes a deep networkcomprising multiple levels of non-linear operations.

Clause 6. The computer-implemented system of any clause herein, whereinthe computing device is further configured to receive other treatmentdata pertaining to at least one other user who uses at least one of thetreatment device or another treatment device to perform anothertreatment plan, wherein the other treatment data comprises at least oneof characteristics of the at least one other user, measurementinformation pertaining to the at least one other user, characteristicsof the at least one of the treatment device and the other treatmentdevice, and at least one aspect of the other treatment plan, wherein theat least one aspect of the other treatment plan corresponds to the atleast one aspect of the treatment plan, and wherein the at least oneaspect of the other treatment plan includes at least one of a treatmentschedule and at least one appointment.

Clause 7. A method comprising: receiving treatment data pertaining to auser who uses a treatment device to perform a treatment plan, whereinthe treatment data comprises at least one of characteristics of theuser, measurement information pertaining to the user, characteristics ofthe treatment device, and at least one aspect of the treatment plan,wherein the at least one aspect of the treatment plan includes at leastone of a treatment schedule and at least one appointment; writing to anassociated memory, configured to be accessed by an artificialintelligence engine, the treatment data, the artificial intelligenceengine being configured to use at least one machine learning model togenerate, using the treatment data, at least one of a treatmentscheduling output prediction and an appointment output; receiving, fromthe artificial intelligence engine, the at least one of the treatmentscheduling output prediction and the appointment output; and selectivelymodifying, using the at least one of the treatment scheduling outputprediction and the appointment output, the at least one aspect of thetreatment plan.

Clause 8. The method of any clause herein, wherein at least some of thetreatment data corresponds to at least some sensor data from a sensorassociated with the treatment device.

Clause 9. The method of any clause herein, wherein at least some of thetreatment data corresponds to at least some sensor data from a sensorassociated with a wearable device worn by the user while the user usesthe treatment device.

Clause 10. The method of any clause herein, wherein measurementinformation includes, while the user uses the treatment device, at leastone of a vital sign of the user, a respiration rate of the user, aheartrate of the user, a temperature of the user, a blood pressure ofthe user, a blood oxygen level (e.g., SpO2) of the user, and microbiomeinformation of the user.

Clause 11. The method of any clause herein, wherein the at least onemachine learning model includes a deep network comprising multiplelevels of non-linear operations.

Clause 12. The method of any clause herein, further comprising receivingother treatment data pertaining to at least one other user who uses atleast one of the treatment device or another treatment device to performanother treatment plan, wherein the other treatment data comprises atleast one of characteristics of the at least one other user, measurementinformation pertaining to the at least one other user, characteristicsof the at least one of the treatment device and the other treatmentdevice, and at least one aspect of the other treatment plan, wherein theat least one aspect of the other treatment plan corresponds to the atleast one aspect of the treatment plan, and wherein the at least oneaspect of the other treatment plan includes at least one of a treatmentschedule and at least one appointment.

Clause 13. The method of any clause herein, further comprisingdetermining, using the artificial intelligence engine using the at leastone machine learning model, whether to group the user with the at leastone other user.

Clause 14. The method of any clause herein, wherein grouping the userwith the at least one other user includes at least associating the atleast one of the treatment schedule and the at least one appointment ofthe treatment plan with the at least one of the treatment schedule andthe at least one appointment of the other treatment plan.

Clause 15. A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to: receivetreatment data pertaining to a user who uses a treatment device toperform a treatment plan, wherein the treatment data comprises at leastone of characteristics of the user, measurement information pertainingto the user, characteristics of the treatment device, and at least oneaspect of the treatment plan, wherein the at least one aspect of thetreatment plan includes at least one of a treatment schedule and atleast one appointment; write to an associated memory, configured to beaccessed by an artificial intelligence engine, the treatment data, theartificial intelligence engine being configured to use at least onemachine learning model to, using the treatment data, generate at leastone of a treatment scheduling output prediction and an appointmentoutput; receive, from the artificial intelligence engine, the at leastone of the treatment scheduling output prediction and the appointmentoutput; and selectively modify, using the at least one of the treatmentscheduling output prediction and the appointment output, the at leastone aspect of the treatment plan.

Clause 16. The computer-readable medium of any clause herein, wherein atleast some of the treatment data corresponds to at least some sensordata from a sensor associated with the treatment device.

Clause 17. The computer-readable medium of any clause herein, wherein atleast some of the treatment data corresponds to at least some sensordata from a sensor associated with a wearable device worn by the userwhile the user uses the treatment device.

Clause 18. The computer-readable medium of any clause herein, whereinmeasurement information includes, while the user uses the treatmentdevice, at least one of a vital sign of the user, a respiration rate ofthe user, a heartrate of the user, a temperature of the user, a bloodpressure of the user, a blood oxygen level (e.g., SpO2) of the user, andmicrobiome information of the user.

Clause 19. The computer-readable medium of any clause herein, whereinthe at least one machine learning model includes a deep networkcomprising multiple levels of non-linear operations.

Clause 20. The computer-readable medium of any clause herein, whereinthe instructions further cause the processing device to receive othertreatment data pertaining to at least one other user who uses at leastone of the treatment device or another treatment device to performanother treatment plan, wherein the other treatment data comprises atleast one of characteristics of the at least one other user, measurementinformation pertaining to the at least one other user, characteristicsof the at least one of the treatment device and the other treatmentdevice, and at least one aspect of the other treatment plan, wherein theat least one aspect of the other treatment plan corresponds to the atleast one aspect of the treatment plan, and wherein the at least oneaspect of the other treatment plan includes at least one of a treatmentschedule and at least one appointment.

Clause 21. The computer-readable medium of any clause herein, whereinthe instructions further cause the processing device to determine, usingthe artificial intelligence engine using the at least one machinelearning model, whether to group the user with the at least one otheruser.

Clause 22. The computer-readable medium of any clause herein, whereingrouping the user with the at least one other user includes at leastassociating the at least one of the treatment schedule and the at leastone appointment of the treatment plan with the at least one of thetreatment schedule and the at least one appointment of the othertreatment plan.

Clause 23. A system comprising: a processing device; and a memoryincluding instructions that, when executed by the processor, cause theprocessor to: receive treatment data pertaining to a user who uses atreatment device to perform a treatment plan, wherein the treatment datacomprises at least one of characteristics of the user, measurementinformation pertaining to the user, characteristics of the treatmentdevice, and at least one aspect of the treatment plan, wherein the atleast one aspect of the treatment plan includes at least one of atreatment schedule and at least one appointment; write to an associatedmemory, configured to be accessed by an artificial intelligence engine,the treatment data, the artificial intelligence engine being configuredto use at least one machine learning model to, using the treatment data,at least one of a treatment scheduling output prediction and anappointment output; receive, from the artificial intelligence engine,the at least one of the treatment scheduling output prediction and theappointment output; and selectively modify, using the at least one ofthe treatment scheduling output prediction and the appointment output,the at least one aspect of the treatment plan.

Clause 24. The system of any clause herein, wherein at least some of thetreatment data corresponds to at least some sensor data from a sensorassociated with the treatment device.

Clause 25. The system of any clause herein, wherein at least some of thetreatment data corresponds to at least some sensor data from a sensorassociated with a wearable device worn by the user while the user usesthe treatment device.

Clause 26. The system of any clause herein, wherein measurementinformation includes, while the user uses the treatment device, leastone of a vital sign of the user, a respiration rate of the user, aheartrate of the user, a temperature of the user, a blood pressure ofthe user, a blood oxygen level (e.g., SpO2) of the user, and microbiomeinformation of the user.

Clause 27. The system of any clause herein, wherein the at least onemachine learning model includes a deep network comprising multiplelevels of non-linear operations.

Clause 28. The system of any clause herein, wherein the instructionsfurther cause the processing device to receive other treatment datapertaining to at least one other user who uses at least one of thetreatment device or another treatment device to perform anothertreatment plan, wherein the other treatment data comprises at least oneof characteristics of the at least one other user, measurementinformation pertaining to the at least one other user, characteristicsof the at least one of the treatment device and the other treatmentdevice, and at least one aspect of the other treatment plan, wherein theat least one aspect of the other treatment plan corresponds to the atleast one aspect of the treatment plan, and wherein the at least oneaspect of the other treatment plan includes at least one of a treatmentschedule and at least one appointment.

Clause 29. The system of any clause herein, wherein the instructionsfurther cause the processing device to determine, using the artificialintelligence engine using the at least one machine learning model,whether to group the user with the at least one other user.

Clause 30. The system of any clause herein, wherein grouping the userwith the at least one other user includes at least associating the atleast one of the treatment schedule and the at least one appointment ofthe treatment plan with the at least one of the treatment schedule andthe at least one appointment of the other treatment plan.

The above discussion is meant to be illustrative of the principles andvarious embodiments of the present disclosure. Numerous variations andmodifications will become apparent to those skilled in the art once theabove disclosure is fully appreciated. It is intended that the followingclaims be interpreted to embrace all such variations and modifications.

The various aspects, embodiments, implementations, or features of thedescribed embodiments can be used separately or in any combination. Theembodiments disclosed herein are modular in nature and can be used inconjunction with or coupled to other embodiments.

Consistent with the above disclosure, the examples of assembliesenumerated in the following clauses are specifically contemplated andare intended as a non-limiting set of examples.

What is claimed is:
 1. A computer-implemented system, comprising: atreatment device configured to be manipulated by a user while the userperforms a treatment plan; a patient interface comprising an outputdevice configured to present telemedicine information associated with atelemedicine session; and a computing device configured to: receivetreatment data pertaining to a user who uses the treatment device toperform the treatment plan, wherein the treatment data comprises atleast one of characteristics of the user, measurement informationpertaining to the user, characteristics of the treatment device, and atleast one aspect of the treatment plan, wherein the at least one aspectof the treatment plan includes at least one of a treatment schedule andat least one appointment; write to an associated memory, configured tobe accessed by an artificial intelligence engine, the treatment data,the artificial intelligence engine being configured to use at least onemachine learning model to, using the treatment data, generate at leastone of a treatment scheduling output prediction and an appointmentoutput; receive, from the artificial intelligence engine, the at leastone of the treatment scheduling output prediction and the appointmentoutput; and selectively modify, using the at least one of the treatmentscheduling output prediction and the appointment output, the at leastone aspect of the treatment plan.
 2. The computer-implemented system ofclaim 1, wherein at least some of the treatment data corresponds to atleast some sensor data from a sensor associated with the treatmentdevice.
 3. The computer-implemented system of claim 1, wherein at leastsome of the treatment data corresponds to at least some sensor data froma sensor associated with a wearable device worn by the user while theuser uses the treatment device.
 4. The computer-implemented system ofclaim 1, wherein measurement information includes, while the user usesthe treatment device, at least one of a vital sign of the user, arespiration rate of the user, a heartrate of the user, a temperature ofthe user, a blood pressure of the user, a blood oxygen level (e.g.,SpO2) of the user, and microbiome information of the user.
 5. Thecomputer-implemented system of claim 1, wherein the at least one machinelearning model includes a deep network comprising multiple levels ofnon-linear operations.
 6. The computer-implemented system of claim 1,wherein the computing device is further configured to receive othertreatment data pertaining to at least one other user who uses at leastone of the treatment device or another treatment device to performanother treatment plan, wherein the other treatment data comprises atleast one of characteristics of the at least one other user, measurementinformation pertaining to the at least one other user, characteristicsof the at least one of the treatment device and the other treatmentdevice, and at least one aspect of the other treatment plan, wherein theat least one aspect of the other treatment plan corresponds to the atleast one aspect of the treatment plan, and wherein the at least oneaspect of the other treatment plan includes at least one of a treatmentschedule and at least one appointment.
 7. A method comprising: receivingtreatment data pertaining to a user who uses a treatment device toperform a treatment plan, wherein the treatment data comprises at leastone of characteristics of the user, measurement information pertainingto the user, characteristics of the treatment device, and at least oneaspect of the treatment plan, wherein the at least one aspect of thetreatment plan includes at least one of a treatment schedule and atleast one appointment; writing to an associated memory, configured to beaccessed by an artificial intelligence engine, the treatment data, theartificial intelligence engine being configured to use at least onemachine learning model to generate, using the treatment data, at leastone of a treatment scheduling output prediction and an appointmentoutput; receiving, from the artificial intelligence engine, the at leastone of the treatment scheduling output prediction and the appointmentoutput; and selectively modifying, using the at least one of thetreatment scheduling output prediction and the appointment output, theat least one aspect of the treatment plan.
 8. The method of claim 7,wherein at least some of the treatment data corresponds to at least somesensor data from a sensor associated with the treatment device.
 9. Themethod of claim 7, wherein at least some of the treatment datacorresponds to at least some sensor data from a sensor associated with awearable device worn by the user while the user uses the treatmentdevice.
 10. The method of claim 7, wherein measurement informationincludes, while the user uses the treatment device, at least one of avital sign of the user, a respiration rate of the user, a heartrate ofthe user, a temperature of the user, a blood pressure of the user, ablood oxygen level (e.g., SpO2) of the user, and microbiome informationof the user.
 11. The method of claim 7, wherein the at least one machinelearning model includes a deep network comprising multiple levels ofnon-linear operations.
 12. The method of claim 7, further comprisingreceiving other treatment data pertaining to at least one other user whouses at least one of the treatment device or another treatment device toperform another treatment plan, wherein the other treatment datacomprises at least one of characteristics of the at least one otheruser, measurement information pertaining to the at least one other user,characteristics of the at least one of the treatment device and theother treatment device, and at least one aspect of the other treatmentplan, wherein the at least one aspect of the other treatment plancorresponds to the at least one aspect of the treatment plan, andwherein the at least one aspect of the other treatment plan includes atleast one of a treatment schedule and at least one appointment.
 13. Themethod of claim 12, further comprising determining, using the artificialintelligence engine using the at least one machine learning model,whether to group the user with the at least one other user.
 14. Themethod of claim 13, wherein grouping the user with the at least oneother user includes at least associating the at least one of thetreatment schedule and the at least one appointment of the treatmentplan with the at least one of the treatment schedule and the at leastone appointment of the other treatment plan.
 15. A tangible,non-transitory computer-readable medium storing instructions that, whenexecuted, cause a processing device to: receive treatment datapertaining to a user who uses a treatment device to perform a treatmentplan, wherein the treatment data comprises at least one ofcharacteristics of the user, measurement information pertaining to theuser, characteristics of the treatment device, and at least one aspectof the treatment plan, wherein the at least one aspect of the treatmentplan includes at least one of a treatment schedule and at least oneappointment; write to an associated memory, configured to be accessed byan artificial intelligence engine, the treatment data, the artificialintelligence engine being configured to use at least one machinelearning model to, using the treatment data, generate at least one of atreatment scheduling output prediction and an appointment output;receive, from the artificial intelligence engine, the at least one ofthe treatment scheduling output prediction and the appointment output;and selectively modify, using the at least one of the treatmentscheduling output prediction and the appointment output, the at leastone aspect of the treatment plan.
 16. The computer-readable medium ofclaim 15, wherein at least some of the treatment data corresponds to atleast some sensor data from a sensor associated with the treatmentdevice.
 17. The computer-readable medium of claim 15, wherein at leastsome of the treatment data corresponds to at least some sensor data froma sensor associated with a wearable device worn by the user while theuser uses the treatment device.
 18. The computer-readable medium ofclaim 15, wherein measurement information includes, while the user usesthe treatment device, at least one of a vital sign of the user, arespiration rate of the user, a heartrate of the user, a temperature ofthe user, a blood pressure of the user, a blood oxygen level (e.g.,SpO2) of the user, and microbiome information of the user.
 19. Thecomputer-readable medium of claim 15, wherein the at least one machinelearning model includes a deep network comprising multiple levels ofnon-linear operations.
 20. The computer-readable medium of claim 15,wherein the instructions further cause the processing device to receiveother treatment data pertaining to at least one other user who uses atleast one of the treatment device or another treatment device to performanother treatment plan, wherein the other treatment data comprises atleast one of characteristics of the at least one other user, measurementinformation pertaining to the at least one other user, characteristicsof the at least one of the treatment device and the other treatmentdevice, and at least one aspect of the other treatment plan, wherein theat least one aspect of the other treatment plan corresponds to the atleast one aspect of the treatment plan, and wherein the at least oneaspect of the other treatment plan includes at least one of a treatmentschedule and at least one appointment.
 21. The computer-readable mediumof claim 20, wherein the instructions further cause the processingdevice to determine, using the artificial intelligence engine using theat least one machine learning model, whether to group the user with theat least one other user.
 22. The computer-readable medium of claim 21,wherein grouping the user with the at least one other user includes atleast associating the at least one of the treatment schedule and the atleast one appointment of the treatment plan with the at least one of thetreatment schedule and the at least one appointment of the othertreatment plan.
 23. A system comprising: a processing device; and amemory including instructions that, when executed by the processor,cause the processor to: receive treatment data pertaining to a user whouses a treatment device to perform a treatment plan, wherein thetreatment data comprises at least one of characteristics of the user,measurement information pertaining to the user, characteristics of thetreatment device, and at least one aspect of the treatment plan, whereinthe at least one aspect of the treatment plan includes at least one of atreatment schedule and at least one appointment; write to an associatedmemory, configured to be accessed by an artificial intelligence engine,the treatment data, the artificial intelligence engine being configuredto use at least one machine learning model to, using the treatment data,at least one of a treatment scheduling output prediction and anappointment output; receive, from the artificial intelligence engine,the at least one of the treatment scheduling output prediction and theappointment output; and selectively modify, using the at least one ofthe treatment scheduling output prediction and the appointment output,the at least one aspect of the treatment plan.
 24. The system of claim23, wherein at least some of the treatment data corresponds to at leastsome sensor data from a sensor associated with the treatment device. 25.The system of claim 23, wherein at least some of the treatment datacorresponds to at least some sensor data from a sensor associated with awearable device worn by the user while the user uses the treatmentdevice.
 26. The system of claim 23, wherein measurement informationincludes, while the user uses the treatment device, least one of a vitalsign of the user, a respiration rate of the user, a heartrate of theuser, a temperature of the user, a blood pressure of the user, a bloodoxygen level (e.g., SpO2) of the user, and microbiome information of theuser.
 27. The system of claim 23, wherein the at least one machinelearning model includes a deep network comprising multiple levels ofnon-linear operations.
 28. The system of claim 23, wherein theinstructions further cause the processing device to receive othertreatment data pertaining to at least one other user who uses at leastone of the treatment device or another treatment device to performanother treatment plan, wherein the other treatment data comprises atleast one of characteristics of the at least one other user, measurementinformation pertaining to the at least one other user, characteristicsof the at least one of the treatment device and the other treatmentdevice, and at least one aspect of the other treatment plan, wherein theat least one aspect of the other treatment plan corresponds to the atleast one aspect of the treatment plan, and wherein the at least oneaspect of the other treatment plan includes at least one of a treatmentschedule and at least one appointment.
 29. The system of claim 28,wherein the instructions further cause the processing device todetermine, using the artificial intelligence engine using the at leastone machine learning model, whether to group the user with the at leastone other user.
 30. The system of claim 29, wherein grouping the userwith the at least one other user includes at least associating the atleast one of the treatment schedule and the at least one appointment ofthe treatment plan with the at least one of the treatment schedule andthe at least one appointment of the other treatment plan.